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Coupling clay swelling properties to hydro-geochemical conditions: a reactive transport modeling approach

Coupling clay swelling properties to hydro-geochemical conditions: a reactive transport modeling approach Christophe Tournassat, BRGM, Orléans France, ISTO, Orléans France, Lawrence Berkeley Laboratory, Berkeley, CA, USA Stéphane Gaboreau, BRGM, Orléans, France Francis Claret, BRGM, Orléans, France Carl Steefel, Lawrence Berkeley Laboratory, Berkeley, CA, USA

The mineralogical and chemical properties of clays have been the subject of longstanding study for the long-term disposal of nuclear wastes in geological repositories. The low permeability of clay materials, including shales, provides at least part of the safety functions for radionuclide contaminants confinement. From a geochemical and mineralogical point of view, the high adsorption capacity of clay minerals adds to the effect of low hydraulic conductivities by greatly increasing the retardation of radionuclides and other contaminants, making clays ideal where isolation from the biosphere is desired. While their low permeability and high adsorption capacity are widely acknowledged, it is clear nonetheless that there is a need for an improved understanding of how the chemical and mineralogical properties of shales impact their macroscopic properties. It is at the pore-scale that the chemical properties of clay minerals become important since their electrostatic properties can play a large role. The negative electrostatic potential field at the clay mineral surfaces results in the presence of porosity domains where electroneutrality is not achieved: cations are attracted by the surfaces while anions are repulsed from them, resulting in the presence of a diffuse ion swarmor diffuse layer. Numerical methods for modeling macroscopic properties of clay media with the consideration of the presence of a diffuse ion swarm have met a growing interest in diverse communities in the past years. In this presentation we will highlight the complex interplays of mineralogical, chemical and microstructural characteristics of clay materials that are ultimately responsible for a remarkable array of coupled macro-scale properties, including swelling. A coupling scheme for predicting swelling pressure as a function of chemical conditions will be presented in the framework of a reactive transport modeling approach.


Liliana Villafaña-López

Erika Clavijo Rivera

Shuli Liu

Estelle Couallier
email: estelle.couallier@univ-nantes.fr

Matthieu Frappart

Shear-enhanced membrane filtration of model and real microalgae extracts for lipids recovery in biorefinery context

Keywords: Microalgae, Lipids, Rotating disk filtration, Ultrafiltration, Oil-in-water emulsion

Introduction

Satisfying the increasing world energy demand with renewable natural resources has significant environmental benefits, that are of great importance to both researchers and entrepreneurs alike. In this context, a very attractive area of research is the cultivation and harvest of microalgae for biodiesel production. A large variety of microalgae has been characterized with regard to cultivation conditions, biomass productivity, lipid content, and lipid productivity [START_REF] Chun-Yen | Cultivation, photobioreactor design and harvesting of microalgae for biodiesel production: A critical review[END_REF]. Between the different varieties of microalgae, the Parachlorella kessleri species stand out for their high lipid production; in addition, the lipid production of this specific species has been successfully scaled to the semi-industrial level [START_REF] Ota | Highly efficient lipid production in the green alga parachlorella kessleri: Draft genome and transcriptome endorsed by whole-cell 3d ultrastructure[END_REF]) with promising results. Several studies concerning the dilution of nutrient media [START_REF] Li | The microalga parachlorella kessleri-a novel highly efficient lipid producer[END_REF], variation of carbon sources (Hamza et al., 2013), and macronutrient limitation [START_REF] Ota | Highly efficient lipid production in the green alga parachlorella kessleri: Draft genome and transcriptome endorsed by whole-cell 3d ultrastructure[END_REF] have demonstrated that nitrogen limitation is (at the moment) the most widely used method to induce lipid overproduction [START_REF] Li | The microalga parachlorella kessleri-a novel highly efficient lipid producer[END_REF].

A crucial step in biodiesel production is the cell disruption and lipid extraction from the harvested cells. Since lipids are intracelular compounds, cell disruption is absolutely necessary to increase the amount of lipids recovered [START_REF] Lee | Disruption of microalgal cells for the extraction of lipids for biofuels: Processes and specific energy requirements[END_REF]; the extraction efficiency, and the lipid integrity depend on the disruption processes being used [START_REF] Byreddy | Comparison of cell disruption methods for improving lipid extraction from thraustochytrid strains[END_REF]. The disrupted microalgae form a mixture composed of the extracted lipids, among other intracellular components, cell debris, and solvent [START_REF] Halim | Extraction of oil from microalgae for biodiesel production: A review[END_REF]. The extraction of lipids directly from the wet disrupted microalgae cells reduces the required monetary and time investment associated with drying processes ('t [START_REF] Lam | Multi-product microalgae biorefineries: From concept towards reality[END_REF].

The filtration process is an attractive option for the lipid concentration from the wet disrupted cells because it can be automated, and it can treat large volumes [START_REF] Kumar Patel | Separation and fractionation of exopolysaccharides from porphyridium cruentum[END_REF]. Additionally, filtration methods allow for a drastic reduction of the required solvents and chemicals used for extraction [START_REF] Safi | A two-stage ultrafiltration process for separating multiple components of tetraselmis suecica after cell disruption[END_REF]. Operating conditions and feed properties such as the transmembrane pressure (TMP), shear rate, membrane hydrophilicity, initial oil concentration, salinity, and pH all play an important role in membrane separation processes [START_REF] Chakrabarty | Ultrafiltration of stable oil-in-water emulsion by polysulfone membrane[END_REF][START_REF] Li | Recovery of linseed oil dispersed within an oil-in-water emulsion using hydrophilic membrane by rotating disk filtration system[END_REF]. The main drawback of the filtration process is the membrane fouling due to the deposition of microalgae material on the membrane surface and/or inside the membrane pores [START_REF] Waghmare | Concentration andcharacterization ofmicroalgae proteins fromchlorella pyrenoidosa[END_REF][START_REF] Marcati | Extraction and fractionation of polysaccharides and b-phycoerythrin from the microalga porphyridium cruentum by membrane technology[END_REF][START_REF] Kumar Patel | Separation and fractionation of exopolysaccharides from porphyridium cruentum[END_REF]. In recent years, shear-enhanced or dynamic filtration (DF) has emerged as an interesting option that reduces membrane fouling. This technique limits the cake growth at the membrane surface [START_REF] Rios | Dynamic microfiltration in microalgae harvesting for biodiesel production[END_REF][START_REF] Ding | Benefits of high shear rate dynamic nanofiltration and reverse osmosis: A review[END_REF] by increasing the shear rate. This increase can be produced by generating turbulence using a rotating disk over a fixed membrane, or by rotating (or vibrating) the membrane itself [START_REF] Jaffrin | Dynamic shear-enhanced membrane filtration: A review of rotating disks, rotating membranes and vibrating systems[END_REF].

Rotating disk (RD) is a type of DF module that provides high permeate fluxes at a steady shear rate, with good performance and low energy requirements [START_REF] Moulai-Mostefa | Euromed 2006 comparison between rotating disk and vibratory membranes in the ultrafiltration of oil-in-water emulsions[END_REF][START_REF] Jaffrin | A hydrodynamic comparison between rotating disk and vibratory dynamic filtration systems[END_REF][START_REF] Hwang | Fine particle removal from seawater by using cross-flow and rotating-disk dynamic filtration[END_REF]. The aforementioned characteristics make this process a very favorable option for biotechnological applications. For example, a vibrating DF system was used to concentrate lipids from steam exploded biomass by [START_REF] Lorente | Microalgae fractionation using steam explosion, dynamic and tangential cross-flow membrane filtration[END_REF]. A polyethersulfone (PES) membrane with a molecular weight cut off (MWCO) of 5 kDa, and a polyvinylidene fluoride membrane (PVDF, MWCO 100 kDa) were selected to perform the filtration at 5 bar. The flux reached 5 to 65 L h -1 m -2 . However, even with the promising results that the RD filtration can provide towards lipid concentration from disrupted microalgae cells, its usage in microalgae biorefining has been generally left unexplored. If a methodology for carrying out lipid concentration from disrupted cells can be established using membrane technology, this process could be used for obtaining different microalgae fractions with minor modifications.

In this work, the performances of the RD technique applied towards the concentration of lipids from microalgae aqueous extracts were evaluated. A model solution [START_REF] Clavijo | Understanding microalgae lipids recovery by membrane processes: cross flow filtration of a representative synthetic mixture (o/w emulsion) (oral)[END_REF] to test the different polymeric membranes was used due to limited access to large volumes of real microlagae culture. Moreover, the large variability inherent to the real products would have limited the comparisons. The membrane with the best separation characteristics was used to compare the performances between crossflow (CF) filtration and RD filtration. Finally the RD filtration was tested with real microalgae aqueous extracts and compared to the CF filtration of the same products.

Material and methods

Real microalgae extracts

The characteristics of the Parachlorella kessleri culture used in this work was grown in autotrophic starving conditions; two aqueous extracts containing dispersed lipids called supernatant (SN 1 and SN 2 ) were produced after bead milling and centrifugation of two different biomasses.

Model solution preparation

The model solution is an oil in water emulsion. It corresponds to the supernatant of a concentrated pretreated culture, after bead milling and separation of the cell fragments by centrifugation [START_REF] Rivera | Mechanical cell disruption of parachlorella kessleri microalgae: Impact on lipid fraction composition[END_REF][START_REF] Clavijo | Understanding microalgae lipids recovery by membrane processes: cross flow filtration of a representative synthetic mixture (o/w emulsion) (oral)[END_REF]. It contains 2 % w of lipids in an aqueous phase (emulating a fresh water culture medium), with a pH of 7.4 and a conductivity equal to 790 µS cm -1 . The lipid phase contains 70 % w of neutral lipids coming from the mixture of vegetable oils and 30 % w of polar lipids constituted of commercial products. The emulsification was performed with a rotor-stator T-25 ULTRA-TURRAX (IKA). The aqueous phase was mixed with a polar lipid product (15 % w of the total lipid content) at 80 • C. The vegetable oils were mixed with a second polar lipid product (15 % w of the total lipid content) at 80 • C. The aqueous and the oil phases were then mixed (24 000 rpm by 30 min) and then, cooled to room temperature during 20 h with gently stirring at 700 rpm.

Filtration modules

The RD module used in this study is described in detail in Refs. (Bouzerar et al., 2000b) and (Bouzerar et al., 2000a). This RD module is equipped with a disk of 15 cm of diameter that rotates inside a cylindrical housing. This disk has eight vanes that are each 2 mm wide by 6 mm tall. The disk can rotate at a maximum speed of 3000 rpm. The pressure is adjusted by operating a valve on the retentate outlet tubing. The peripheral pressure is measured at the top of the cylindrical housing by a pressure sensor. On the cover of the cylindrical housing (in front of the disk), a single circular membrane with dimensions of 8.4 cm of outer radius and 1.2 cm of inner radius (usable area of 185 cm 2 ) can be placed. Fig. 1a presents a cross-sectional view of the module design and its internal circulation path.

The movement of the disk near the fixed circular membrane creates a maximum shear rate ( γmax ) with turbulent flow, which is calculated as (Bouzerar et al., 2000a;[START_REF] Frappart | Influence of hydrodynamics in tangential and dynamic ultrafiltration systems for microalgae separation[END_REF],

γmax = 0.0296 • ν -4 5 • kω 9 5 • R 8 5 d , (1)
where ν is the kinematic viscosity (m 2 s -1 ), k is the velocity factor, ω is the angular disk velocity (rad s -1 ), and R d is the disk radius (m). kω is the angular velocity of the inviscid fluid.

The CF module is a Rayflow X100 (Orelis-Novasep). This module consists of two restraining plates that can contain up to two flat rectangular membranes, each with a usable area of 130 cm 2 . Filtration pressure is adjusted with a valve, with one pressure sensor placed at the module inlet and another at the retentate outlet. Fig. 1b presents a schematic representation of this module. The maximum shear rate from the laminar flow on the membrane can be calculated as [START_REF] Delaunay | Mapping of protein fouling by ftir-atr as experimental tool to study membrane fouling and fluid velocity profile in various geometries and validation by cfd simulation[END_REF],

γmax = 4 e • v max , (2)
where e = 0.5 mm is the chamber thickness, and v max is the tangential flow velocity obtained in the chamber (m s -1 ). The experimental setup used in this study (the RD and the CF modules) is described in Ref. [START_REF] Frappart | Influence of hydrodynamics in tangential and dynamic ultrafiltration systems for microalgae separation[END_REF]. Table 1 shows the corresponding v max and γmax for the three different setups tested in this work.

Membranes

Four commercial membranes were selected. These membranes were manufactured by Orelis Environnement (Pleiade range), with differing polymer composition, hydrophilicity, and MWCO. The membranes used were a PES (200 kDa), a polyacrylonitrile (PAN 500 kDa), a PVDF (0.4 µm), and another PVDF (1.5 µm). Before being used, each membrane was cut, washed, and compacted in order to eliminate membrane preservatives (glycerol), and to ensure a steady water flux. The cleaning procedure involved the membrane rinsing with ethanol and demineralized water; then, the membrane was submerged for 30 min in demineralized water (refreshed every 10 min). The experimental flux values were adjusted to 30 • C using a correction coefficient related to water viscosity (µ).

Screening of the membranes with the model solution

The permeate flux (J) variation versus the TMP was determined by recirculating the emulsion in a closed system at 30 • C. J was measured for the lowest TMP possible, then the permeate was returned to the feed tank and the TMP was increased with 0.1 bar steps, repeating the same procedure. Once the limiting flux was reached, two additional measurements were performed in order to verify the flux plateau.

Concentration experiment

The concentration experiment was evaluated through the volume reduction ratio (VRR). The emulsion was recirculated at 30 • C, collecting the permeate and returning the retentate back to the feed tank until reaching different VRR. In a batch system concentration, the VRR is defined as,

VRR = V 0 V f , (3)
where V 0 and V f are the initial and final emulsion volumes. Samples of instantaneous retentate, permeate, and collected permeate were taken at each of these points. During this experiment the membrane performance was followed through the flux decline, membrane selectivity and total oil mass accumulated on the membrane. Flux decline was calculated as a function of the emulsion permeate flux (J) and the initial membrane demineralized water flux (J w ) according to the following expression,

% Flux decline = 1 - J J w × 100. ( 4
)
The membrane selectivity was verified through the retention rate (% R), which is a function of the permeate (C p ) and retentate (C r ) concentrations,

% R = 1 - C p C r × 100. ( 5
)
The total oil mass accumulated on the membrane and non-membrane interfaces during the concentration experiment (m Tot VRR ) was calculated as

m Tot VRR = (X 0 M 0 ) -(X r M r ) -(X p M p ) - n i=0 X r m i r + X p m i p , (6)
where X 0 , X r , and X p are the initial, retentate, and permeate mass fractions of oil in the emulsion (g oil /g emulsion ), respectively. Likewise, M 0 , M r , and M p are the equivalent emulsion masses (g). m i r , and m i p are the samples mass (g) taken out of the system for each analysis, and i represents the sample taken out of the system from a total n number of samples. The amount of salt contained in the buffer that was used to prepare the emulsion, was subtracted from the different samples before calculating m Tot VRR . The maximum amount of oil accumulated on the non-membrane interfaces of the RD module was obtained by recirculating the model solution trough the system for 7 h, without a membrane in order to reach equilibrium. A sample was taken at each hour of the experiment, and the oil content was quantified by gravimetric analysis. The maximum amount of oil accumulated at the non-membrane interfaces of the RD module was subtracted from Eq. ( 6). The final amount was divided by the apparent surface area of the membrane (m 2 ), obtaining the quantity of oil accumulated only on the membrane (q mb VRR ) as,

q mb VRR = m Tot VRR -m non-membrane A . (7)

Cleaning procedure

The emulsion elimination from the membrane was performed right after the concentration experiment, once the remaining emulsion was expelled out of the filtration module. The cleaning procedure encompasses a cycle of three steps. First, recirculation of demineralized water (40 For membranes used with the real microalgae extract, another cleaning step was implemented using a commercial alkaline Ultrasil 110 (Ecolab) solution.

A long rinsing with water was performed after each step. The cleanability of the membrane was evaluated using the flux recovery ratio (% FRR) which relates the permeate flux after the cleaning procedure (J r ) and J w as,

% FRR = J r J w × 100. ( 8
)
The membrane was considered clean when the % FRR was greater than 90 %.

Gravimetric analysis

The IR-30 Moisture Analyzer (Denver Instrument) was used to determine the emulsion dry matter, and therefore calculate the mass balance. This device is based on heating the sample with infrared light then determining the remaining amount of dry mass. A measuring accuracy of 0.05 g is reported for an initial sample weight from 5 g to 10 g. Glass fiber pads were used to evenly spread the samples. Prior to the sample analysis, a glass fiber pad and a weighing dish of 90 mm in diameter were dried at 105 • C for 5 min, eliminating any trace of moisture. The samples were dried at 105 • C and automatic drying time. To ensure accuracy all samples were replicated three times.

Droplet size distribution

The droplet size distribution (DSD) of the emulsion was determined with a Mastersizer 3000 laser diffraction particle size analyzer (Malvern). This instrument is equipped with the hydro large volume and small volume wet dispersion units. The device has a detection particle size range of 0.01 µm to 3500 µm. The samples were analyzed (in triplicate) without dilution, using a refraction index of 1.44 and a absorption index of 0.003.

Ultrapure water contact angle

The ultrapure water contact angle (θ w ) quantifies the wettability of a solid surface. The water used has a resistivity of 15 MΩcm and pH=8.35 ± 0.49 at 25 • C. θ w was measured on the four clean and emulsion-fouled membranes. The Drop Shape Analyzer -DSA30A TRACKER instrument was used for this purpose in sessile drop mode. Before the θ w measurement, the fouled membranes were removed from the filtration modules immediately after the concentration experiment in order to maintain the fouled surface. The membrane surface was then gently rinsed with demineralized water to eliminate the excess emulsion. Both membrane samples were dried in an oven for 48 h at 35 • C, followed by 24 h in a desiccator. The membranes were then fastened on glass slides using double-sided tape.

The water drops were dosed using a glass syringe (500 µL) with a disposable stainless steel needle (diameter of 0.5 mm). The approximate volume of each drop was 2.5 µL. θ w was measured 100 ms after drop deposition. The left and right contact angles of each drop were calculated from the digitized image using the DSA software. The profile of the sessile drop was calculated using a tangent method that fits the drop to a general conic equation. The derivative of the equation at the drop baseline is the slope at the three-phase contact point, which is used to determine the contact angle. 12 droplets of the water were deposited on different sections of the same membrane sample, increasing the accuracy of the average value of the contact angle. This technique presents an accuracy of 2 • to 5.5 • (Diagne et al., 2013).

Results and Discussion

3.1. Screening evaluation of the membranes with the model solution

Determination of the critical pressure

The limiting flux (J lim ) is the maximum steady flux achieved by increasing the TMP [START_REF] Hurt | Microfiltration: Effect of channel diameter on limiting flux and serum protein removal1[END_REF]; the TMP value at which this occurs is defined as the maximum pressure (P max ). The critical flux (J c ) can be generally defined as the first permeate flux at which a prominent fouling takes place [START_REF] Hurt | Microfiltration: Effect of channel diameter on limiting flux and serum protein removal1[END_REF], and the corresponding pressure for this flux is called the critical pressure (P c ). This P c corresponds to the TMP at which the normalized flux (J/J w ) begins to decrease, which indicates an increase in membrane fouling. Figs. 2a and 2b feature the average variation of the permeate flux (J), and J/J w as functions of the TMP taken at 30 • C for the PAN 500 kDa and PVDF 0.4 µm membranes. The initial J w was measured and adjusted at 30 • C.

As can be seen in Fig. 2a, the 0.4 µm PVDF membrane presents a J which increases with the TMP; for this membrane, a value of J lim = 66 ± 1 L h -1 m -2 occurs at P max = 0.6 bar. For the PAN membrane, J lim never reaches a steady value in the range of TMP studied in this work; therefore, P max can not be determined. Different fouling behavior of the membranes with similar filtration conditions can be explained considering their pore size distribution and degree of hydrophilicity; these characteristics determine the water and oil droplet permeation. Membrane polarization, attraction/adsorption between the membrane and the oil droplets, oil layer formation at the membrane surface, and the steric effect all play a role in determining the oil droplet retention by the membrane.

Fig. 2b shows that the initial J/J w is close to 1.0 for both membranes. This indicates that the permeate flux is nearly the same as the water flux at low TMP, indicating that fouling is very low. However, the P c of the 0.4 µm PVDF membrane was detected around 0.4 bar; for the 500 kDa PAN membrane, P c was between 0.5 and 0.6 bar. For TMP > P c , the J/J w begins to decrease inversely proportional to the TMP for both membranes, indicating that the membrane fouling has begun. The 500 kDa PAN membrane has a slow, gradual decrease, while the 0.4 µm PVDF membrane has a significantly steeper decline. Table 2 presents the average J w at 1 bar, the J lim , J c along with their variability (σ), P max , and P c values of the different membranes used in this study. The initial water contact angle (θ 0 w ) of the different membranes was measured following the procedure described in Sec. 2.8. Materials with θ w < 45 • are highly-hydrophilic, when 45 • < θ w < 90 • , they have both hydrophilic/hydrophobic properties, and for θ w > 90 • , the materials are hydrophobic ( Świerczyńska et al., 2016).

The 500 kDa PAN membrane presented relatively high P c values along with the lowest θ 0 w value (75.0 ± 2.4). This suggest a high degree of membrane hydrophilicity that might limit fouling formation. The 1.5 µm PVDF membrane also presented relatively high P c values despite its θ 0 w value (82.3 ± 0.7), which indicates hydrophilic/hydrophobic membrane properties. Therefore, the potential accumulation of lipids on the membrane did not have a significant impact on the water permeation and the critical pressure because of the large pore size, that allowed the permeation of water but also some fine oil droplets. During the screening evaluation the 1.5 µm PVDF membrane presented turbid permeates, while transparent permeates were obtained for the remainder of the membranes. 200 kDa PES and 0.4 µm PVDF membranes presented the lowest P c values; therefore, a considerably high membrane fouling was supposed for both cases. For the former membrane, this fouling was mainly due to its small MWCO leading to a high lipids retention and probable adsorption pore blocking or cake formation. In the latter membrane, the fouling was due to its hydrophobic properties (θ 0 w = 95.1 ± 1.2) thus a strong adsorption.

Concentration experiment

The concentration experiment was carried out just after the critical pressure measurement. The TMP of the concentration experiment was 90 % of the P c ; this percentage was selected in order to have the highest permeate flux while avoiding strong membrane fouling. The examples of the evolution of J and its normalized representation versus the VRR during the concentration experiments for the 500 kDa PAN and 0.4 µm PVDF membranes are presented in Fig. 3.

J and J/J w decreased with the VRR; this behavior is similar for all the membranes studied in this work. The 500 kDa PAN and 0.4 µm PVDF membrane presented an initial J/J w around 0.9 and 0.7, respectively. At the highest VRR the normalized flux decreased 22 % for the former membrane, and 14 % for the latter membrane. Table 3 shows the % Flux decline, % FRR, q mb VRR , θ 0 w , and water contact angle after the concentration experiment (θ VRR w ) for all membranes at an average VRR = 4.3 ± 0.1. As already observed during previous critical pressure measurements, concentration experiments do not modify the retention behavior of the membrane. Oil retention was higher than 95 % for each membrane. An increased hydrophilicity (lower θ VRR w value) in comparison with the clean membranes (θ 0 w ) was observed for all the membranes. The 200 kDa PES membrane presented a very high % Flux decline along with the lowest % FRR. This suggests a high degree of membrane fouling that is difficult to remove which has a significant impact on the membrane properties. In fact, this membrane presented the greatest increase in hydrophilicity, 28 % that might be due to the adsorption of the polar lipids present in the model solution formulation. These results suggest that the oil droplet accumulation on the membrane is responsible for the strong fouling of the membrane.

The 500 kDa PAN membrane presented a low value of % Flux decline, and a total % FRR. This membrane behavior suggests the formation of a fouling layer that is easily removed. In fact, a large amount of the oil droplets accumulated on the membrane surface were almost completely eliminated when gently rinsed before the measurement of θ w . The hydrophilic properties of the fouling may also be near from the membrane properties. This led to a very small increase in the membrane hydrophilicity of only 8 %. The large quantity of accumulated matter had a low impact on the filtration performances. This indicates a different structure of the fouling. The 0.4 µm PVDF membrane presented a % Flux decline between 42 and 45 %, and a % FRR < 90 % that suggests an irreversible membrane fouling. This membrane presented the highest amount of accumulated oil and an increased hydrophilicity of 11 %; this membrane changed from hydrophobic to hydrophilic due to the accumulation of polar lipids. This performance indicated a strong fouling, which was expected due to the initial hydrophobic properties and MWCO of the membrane that led to pore blocking and cake formation. The 1.5 µm PVDF membrane presented a % Flux decline between 23 % to 85 %, along with a % FRR < 90 %. The lowest quantity of oil accumulated was calculated for this membrane. A turbid permeate was observed for the PVDF 1.5 µm membrane. Because of the MWCO of the membrane, some oil droplets could be accumulated in the pores reducing their size and provoking a fast, irreversible, and internal membrane fouling (pore blocking and pore constriction). A small amount of polar lipids were present on the membrane surface, leading to an increase in the hydrophilicity of only 2 %. Considering all the previous results, the membrane that presented the best oil separation performances was the 500 kDa PAN membrane. This membrane had a high range of P c , and the highest initial hydrophilicity level amongst the membranes included in this study. During the concentration experiment, it presented complete oil retention, the lowest % Flux decline, and was the only one with a % FRR > 90 %. Also, the oil accumulated on the membrane (polar lipids) improved the hydrophilicity. In addition to these experimental observations, other works have demonstrated that this type of membrane (in both crossflow and dynamic filtration modules) is well suited for microalgae suspensions, as it presents less adsorption of cells or cellular compounds [START_REF] Rossi | Harvesting of cyanobacterium arthrospira platensis using organic filtration membranes[END_REF][START_REF] Rossignol | Membrane technology for the continuous separation microalgae/culture medium: compared performances of cross-flow microfiltration and ultrafiltration[END_REF][START_REF] Frappart | Influence of hydrodynamics in tangential and dynamic ultrafiltration systems for microalgae separation[END_REF]. The same membranes were tested using the crossflow filtration technique, confirming the classification of the membranes with respect to the flux decline described above [START_REF] Clavijo | Understanding microalgae lipids recovery by membrane processes: cross flow filtration of a representative synthetic mixture (o/w emulsion) (oral)[END_REF].

Influence of the hydrodynamics on the filtration of the model solution

The hydrodynamic impact on the filtration performance was studied using the CF and RD modules along with the previously selected 500 kDa PAN membrane. Three different setups were tested in this work and are shown Table 4. The permeate flux measurements versus the TMP were carried out as described in Sec. 2.5 for the CF filtration and RD filtration at 365 rpm. The results collected were compared with the best result obtained for the RD filtration at 800 rpm, described in the previous section. J lim , P max , J c , and P c values for the three different setups are also presented in Table 4.

For both RD speeds, J never reached a steady value in the range of TMP studied in this work, therefore J lim and P max could not be measured. Only the CF filtration presented J lim and P max values. It can be seen that P c increased with the v max . The concentration experiments were carried out just after the P c determination, as described in Sec. 3.1.2. The TMP of the concentration experiment was 90 % of the P c . The three setups presented a similar J evolution having an average value of 28 ± 16 m s -1 at a VRR = 1; after this, J began to decrease with the VRR. Fig. 4 shows the evolution of the normalized J/J w versus the VRR with the 500 kDa PAN membrane during the CF and the RD filtrations.

Membrane fouling can be limited by high shear rates [START_REF] Zhao | Microalgae harvesting by an axial vibration membrane: The mechanism of mitigating membrane fouling[END_REF][START_REF] Zhang | Investigation of membrane fouling mechanisms using blocking models in the case of shear-enhanced ultrafiltration[END_REF][START_REF] Goh | Membrane fouling in desalination and its mitigation strategies[END_REF]; therefore, the smallest decrease in the J/J w ratio was expected for the RD filtration at 800 rpm since the γmax was more than four times larger than the CF filtration and the RD filtration at 365 rpm. On the other hand, the CF filtration and the RD filtration at 365 rpm had the same γmax value; however, the CF filtration presented a laminar regime while the RD filtration had a turbulent one. This turbulent flow can more efficiently enhance the convection of particles away from the membrane [START_REF] Frappart | Influence of hydrodynamics in tangential and dynamic ultrafiltration systems for microalgae separation[END_REF][START_REF] Du | Control of submerged hollow fiber membrane fouling caused by fine particles in photocatalytic membrane reactors using bubbly flow: Shear stress and particle forces analysis[END_REF][START_REF] Zhang | Investigation of membrane fouling mechanisms using blocking models in the case of shear-enhanced ultrafiltration[END_REF][START_REF] Abid | A review of efforts to reduce membrane fouling by control of feed spacer characteristics[END_REF][START_REF] Goh | Membrane fouling in desalination and its mitigation strategies[END_REF]. As can be seen in Fig. 4, the J/J w ratio decreased progressively with the VRR for the three different setups. Table 5 presents % Flux decline and q mb VRR measured during the concentration experiment at a VRR = 3.8 ± 0.3.

The retention rate and the cleanability were higher than 97 % and 90 %, respectively, for all experiments; the latter suggests a totally reversible membrane fouling. The CF filtration presented the highest q mb VRR indicating the presence of a strong fouling on the membrane. As mentioned above, this low performance of the CF filtration can be explained by its laminar regime which allows a large accumulation of droplets on the membrane. The RD filtration at 365 rpm led to a q mb VRR value lower than at 800 rpm but the same flux decline. This may be due to different fouling mechanisms.

Effect on the hydrodynamics on the DSD

The applied pressure in the system has a huge impact on the oil droplets [START_REF] Hong | Factors affecting membrane coalescence of stable oil-in-water emulsions[END_REF]. In particular, coalescence is the joining of two or more (micron and submicron) oil droplets to form a single droplet with a larger diameter than the original [START_REF] Chakrabarty | Ultrafiltration of stable oil-in-water emulsion by polysulfone membrane[END_REF]. The effect of the CF filtration and the RD filtration on the coalescence or division of the oil droplets during the emulsion filtration was followed using the technique described in Sec. 2.7. The droplet size can be represented as a differential distribution, which represents the frequency of each size. The mode is the most repeated droplet diameter and corresponds to the highest point of the differential curve [START_REF] Flügel | Microfacies of Carbonate Rocks. Analysis, Interpretation and A cation[END_REF]. The evolution of the retentate volume DSD as a function of the VRR is shown in Fig. 5. It was not possible to measure the DSD in the permeates because of the low concentration of oil droplets.

The DSD presented a multimodal distribution, but the DSD during CF filtration changed the least. The major mode diameter during the CF filtration was maintained around 1.28 ± 0.01 µm. The major mode diameter during the RD filtration at 365 rpm increased with the VRR from 1.59 to 3.93 µm. The volume fraction of the droplets with a diameter around 1.5 µm decreased and the volume fraction of the droplets with a diameter above 4 µm increased with VRR, indicating coalescence. On the contrary, during the RD filtration at 800 rpm the droplets with a diameter around 15 µm disappeared, indicating droplet division. The differences in the two DSD from the RD filtration were due to the different shear rate applied near the membrane. It is possible that the shear rate at 365 rpm (16 000 s -1 ), facilitated the oil droplet coalescence, while the higher shear rate at 800 rpm (66 000 s -1 ) divided the oil droplets. The laminar flow during CF filtration did not significantly affect the DSD.

The RD filtration offered better performances over the CF filtration because it limited membrane fouling, thus achieving relatively high ranges of P c and permeation flux. During the concentration experiment, this RD filtration presented an almost complete retention of lipids and a full cleanability. However, oil droplet coalescence seemed to be favored at 365 rpm, while droplet breakage tended to occur at 800 rpm. This may have an impact on the fouling mechanisms. These results confirm former studies which demonstrated that the RD filtration presents increased performances compared to the CF filtration when applied to microalgae fractionation [START_REF] Frappart | Influence of hydrodynamics in tangential and dynamic ultrafiltration systems for microalgae separation[END_REF][START_REF] Lorente | Microalgae fractionation using steam explosion, dynamic and tangential cross-flow membrane filtration[END_REF].

Filtration of real microalgae extract

Two Parachlorella kessleri cultures (1 g L -1 ) were used to produce two batches of clarified supernatant, SN 1 and SN 2 , dedicated to filtration tests at the pilot scale. The supernatants were filtered using the RD module equipped with a 500 kDa PAN membrane at 800 rpm. The composition of the supernatants, retentates (RET 1 and RET 2 ) and permeates (PERM 1 and PERM 2 ) are described in Table 6. The variations between SN 1 and SN 2 are due to differences in biomass and pretreatment [START_REF] Liu | Membrane filtration for the recovery of lipids from microalgae extracts (oral)[END_REF].

After critical pressure measurements, the chosen TMP (90 % of the P c ) were 0.4 bar and 0.7 bar for SN 1 and SN 2 , respectively. The normalized representation of the permeate flux of SN 1 , SN 2 , and model solution versus the VRR with the 500 kDa PAN membrane in the RD module ( γmax =66 000 s -1 ) and CF module ( γmax =16 000 s -1 ) are presented in Fig. 6. The J/J w ratio was used for comparison due to the inherent variability of the individual membrane pieces.

The flux from the RD filtration with the supernatants was two times (SN 1 ) or three times (SN 2 ) superior to the water flux, which was not the case of the model solution. This means that during the filtration of the supernatants, the membrane permeability and hydrophilicity were strongly enhanced. Additionally, minimal membrane fouling was observed, thus reducing the needed time to reach the highest VRR. These changes in the membrane properties may be due to the adsorption of some of the polar compounds (possibly proteins or polar lipids) present in the supernatants. Therefore, the calculation of the membrane cleanability was not realistic under such conditions. The J/J w values in both cases are higher than those presented by Lorente et al. (2017) (J/J w < 0.2). Their choice of the TMP (5 bar, probably above the P c ) coupled with a shear rate that may induce droplets division, and the membrane nature (PES or PVDF), have probably limited the flux performance. These results demonstrate the crucial choice of the filtration operating conditions.

The particle size distribution of the supernatants show that the particle diameter decreases very slightly, without significant changes (not shown). The retention rates of lipids, proteins, polysaccharides, and salts were calculated for the RD filtration at VRR=2. For both supernatants the lipids are totally retained, while the salts have a retention rate between 11 % and 15 %. The retention rates of proteins for SN 1 is 100 %, while for the SN 2 is 78 %. The polysaccharides are retained 48 % for SN 1 and 52 % for SN 2 . These percentages depend on the supernatant and the pretreatment steps. They are not far from the retention rates obtained in CF filtration. Finally, the RD and CF filtrations of supernatants were compared confirming that the RD filtration presents better flux and J/J w performance than the CF filtration.

Conclusions

This study first evaluated different commercial membranes, filtration regimes, and shear rates using an emulsion that simulates the characteristics of real microalgae extracts. The 500 kDa PAN membrane presented the best performances (oil retention, water permeation and cleanability) and the RD filtration offered better performances than the CF filtration. The performance of the RD module was then evaluated with real microalgae extracts, confirming those results. The importance of the operating conditions was demonstrated. Further investigation will allow finding the best pretreatment and filtration coupling for lipids and hydrophilic compounds separation. Supplementary Material for this work can be found with the online version of the paper. It includes the preparation and characteristics of the Parachlorella kessleri culture used in this work and the parameters (TMP, fluxes and permeabilities) registered during the filtration of real supernatants. γmax , % Flux decline and q mb VRR (reported with uncertainty propagation) measured during the concentration experiment with the 500 kDa PAN membrane and both modules at the final average VRR = 3.8 ± 0.3 and 30 • C. . . . . . . . . . . . . . . 36 6 Composition of the supernatants (SN 1 and SN 2 ) of bead-milled Parachlorella kessleri cultures before filtration, and composition of the retentates (RET 1 and RET 2 ) and permeates (PERM 1 and PERM 2 ) sampled at VRR=2 with the RD module at γmax =66 000 s -1 . The standard deviation (σ) was calculated from three independent measurements. . . . . . . . . . . . . . 37

1

  Filtration modules used in this study. . . . . . . . . . . . . . .25 2 Flux (J) and normalized flux (J/J w ) of the model solution permeate versus TMP for the PAN 500 kDa and PVDF 0.4 µm membranes on the RD module with γmax =66 000 s -1 . The error bars correspond to the standard deviation calculated from three independent measurements. . . . . . . . . . . . . . . . .26 3 Evolution of the permeate flux (J) and its normalized representation (J/J w ) versus the VRR for the PAN 500 kDa and PVDF 0.4 µm membranes during the RD filtration ( γmax =66 000 s -1 ). The error bars correspond to the uncertainty propagation from three independent measurements. . . . . . . . . . . . . . . . . 27 4 Normalized representation of the permeate flux versus the VRR with the 500 kDa PAN membrane at 30 • C during the RD and the CF filtrations. The error bars correspond to the uncertainty propagation from three independent measurements. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 28 5 Volume droplet size distribution as a function of the VRR in the retentate sample using the 500 kDa PAN membrane for the CF and RD filtrations. . . . . . . . . . . . . . . . . . . . . 29 6 Normalized representation of the permeate flux of the supernatants, SN 1 and SN 2 , and the model solution (MS) versus the VRR with the 500 kDa PAN membrane and both filtration modules; RD module ( γmax =66 000 s -1 ) and CF module ( γmax =16 000 s -1 ) The error bars correspond to the uncertainty propagation from three independent measurements. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 30 (a) Cross-sectional view of the rotating disk module (Frappart et al., 2006). (b) Schematic of the crossflow module.

Figure 1 :

 1 Figure 1: Filtration modules used in this study.

Figure 2 : 1 JFigure 3 :Figure 4 :

 2134 Figure2: Flux (J) and normalized flux (J/J w ) of the model solution permeate versus TMP for the PAN 500 kDa and PVDF 0.4 µm membranes on the RD module with γmax =66 000 s -1 . The error bars correspond to the standard deviation calculated from three independent measurements.

Figure 5 :

 5 Figure 5: Volume droplet size distribution as a function of the VRR in the retentate sample using the 500 kDa PAN membrane for the CF and RD filtrations.

Table Captions 1

 Captions Working parameters selected for both the RD and CF modules. Parameters for the RD module were obtained from Eq. (1), while Eq. (2) was used for the CF parameters. . . . . . . 32 2 J w (1 bar), J lim , P max , J c , and P c values for the different membranes studied during the RD filtration with γmax =66 000 s -1 at 30 • C. The standard deviation (σ) is calculated from three independent measurements. . . . . . . . . . . . . . . . . . . . . 33 3 % Flux decline, % FRR, q mb VRR , θ 0 w , and θ VRR w at a VRR of 4.3 ± 0.1, during the RD filtration at γmax =66 000 s -1 , and 30 • C. q mb VRR is reported with uncertainty propagation, while θ 0 w and θ VRR w have standard deviations (σ) calculated from ten independent measurements. . . . . . . . . . . . . . . . . . . . . . 34 4 v max , γmax , J lim , P max , J c , and P c values for the 500 kDa PAN membrane at 30 • C during the CF and the RD filtrations. The standard deviation (σ) is calculated from three independent measurements. . . . . . . . . . . . . . . . . . . . . . . . . . . 35 5

Table 1 :

 1 Working parameters selected for both the RD and CF modules. Parameters for the RD module were obtained from Eq. (1), while Eq. (2) was used for the CF parameters.

	Module	v max (m s -1 ) γmax (s -1 )
	RD (365 rpm)	2.5	16000
	RD (800 rpm)	5.4	66000
	CF	2.0	16000

Table 2 :

 2 J w (1 bar), J lim , P max , J c , and P c values for the different membranes studied during the RD filtration with γmax =66 000 s -1 at 30 • C. The standard deviation (σ) is calculated from three independent measurements.

Table 3 :

 3 % Flux decline, % FRR, q mb VRR , θ 0 w , and θ VRR w at a VRR of 4.3 ± 0.1, during the RD filtration at γmax =66 000 s -1 , and 30 • C. q mb VRR is reported with uncertainty propagation, while θ 0 w and θ VRR

	Polymer MWCO Flux decline FRR (%) (%)	q mb VRR (g m -2 )	θ 0 w ±σ ( • )	θ VRR w ( • ) ± σ
	PES	200 kDa	75-84	20	134 ± 19 79.4 ± 0.5 57.3 ± 0.6
	PAN	500 kDa	14-40	100 378 ± 15 75.0 ± 2.4 69.2 ± 2.1
	PVDF	0.4 µm	42-45	72	429 ± 18 95.1 ± 1.2 84.5 ± 0.5
	PVDF	1.5 µm	23-85	47	6 ± 18 82.3 ± 0.7 80.6 ± 0.7

w have standard deviations (σ) calculated from ten independent measurements.

Table 5 :

 5 γmax , % Flux decline and q mb VRR (reported with uncertainty propagation) measured during the concentration experiment with the 500 kDa PAN membrane and both modules at the final average VRR = 3.8 ± 0.3 and 30 • C.

	Filtration module γmax Flux decline (s -1 ) (%)	q mb VRR (g m -2 )
	CF	16000	59	676 ± 231
	RD (365 rpm)	16000	13	24 ± 23
	RD (800 rpm)	66000	14	378 ± 15

Table 6 :

 6 Composition of the supernatants (SN 1 and SN 2 ) of bead-milled Parachlorella kessleri cultures before filtration, and composition of the retentates (RET 1 and RET 2 ) and permeates (PERM 1 and PERM 2 ) sampled at VRR=2 with the RD module at γmax =66 000 s -1 . The standard deviation (σ) was calculated from three independent measurements. ± 0.1 PERM 2 1.00 ± 0.04 0.00 ± 0.00 0.05 ± 0.14 0.16 ± 0.02 947 ± 5 7.3 ± 0.1

		Dry matter	Lipids	Proteins	Sugar	Conductivity	pH
		(g L -1 )	(g L -1 )	(g L -1 )	(g L -1 )	(µS cm -1 )
	SN 1	2.00 ± 0.10 0.16 ± 0.02 0.38 ± 0.05 0.45 ± 0.04	1049 ± 8	7.6 ± 0.1
	RET 1	2.80 ± 0.05 0.33 ± 0.05 0.62 ± 0.01 0.42 ± 0.01	1231 ± 6	7.6 ± 0.1
	PERM 1 1.44 ± 0.09 0.00 ± 0.00 0.00 ± 0.02 0.22 ± 0.00	1092 ± 6	7.6 ± 0.1
	SN 2	1.34 ± 0.10 0.17 ± 0.03 0.19 ± 0.03 0.32 ± 0.07	993 ± 20	7.2 ± 0.1
	RET 2	1.76 ± 0.04 0.30 ± 0.03 0.23 ± 0.01 0.33 ± 0.01	1119 ± 6	7.3

Acknowledgments

The authors gratefully acknowledge partial financial support from CONACYT-Mexico Grant No. 262944. This work was also funded by CNRS, France (interdisciplinary project Algues-Molécules-Territoire, 2014-2017), GIS Europôle Mer, France (Sciences et Ingénieries Marines, 2016-2017), and ADEME. The authors would also like to thank Guillaume Roelens, Delphine Drouin (GEPEA), Marie Cueff, Jordan Tallec (GEPEA, Algosolis) and Laurence Lavenant (INRA BIA) for their active participation in the preparation of the microalgae extracts and their analysis.

The error bars correspond to the uncertainty propagation from three independent measurements.

Polymer MWCO

Module


Imen Akermi
email: imen.akermi@irit.fr

Rim Faiz
email: rim.faiz@ihec.rnu.tn

Situation Assessment for Non-Intrusive Recommendation

With the rapid growth of mobile applications, the user is increasingly confronted with a lot of information and tend to reject notifications sent by applications installed within his/her mobile device. This rejection affects the performance of many systems, especially proactive recommender systems. Therefore, it is no longer enough for a recommender system to determine what to recommend according to users' needs, but it also has to deal with the risk of disturbing the user during the recommendation process. We believe that the several embedded applications within the user's device along with other parameters could help understand and assess the user's interruptibility in some situations. In this paper, we address intrusiveness within a proactive recommendation approach that makes use of the user's context and the applications embedded within the user's mobile device in order to assess the intrusiveness level of a given situation before recommending.

I. INTRODUCTION

With the development of web platforms and new technologies, the interest in recommender systems has significantly increased and has spread to cover multiple domains such as movies 1 , tourism2 and videos 3 . Traditional recommender systems aim at providing relevant information to users. With the recent spread of mobile devices (smartphones and tablets), we notice that recommender systems are progressively adapting to pervasive environments in order to deliver not only relevant information to users but also when it is most needed. Indeed the amount of the contextual information provided by the mobile devices sensors such as temperature, GPS, accelerometer, etc. help to understand the users' needs and deliver recommendations without the user's request. This is called context-aware proactive (just-in-time) recommendation or zero-query search. Proactive or Just-In-Time recommender systems involve all systems able to provide recommendations tailored to the preferences and needs of users in order to help them access useful and interesting resources within a large volume of data. The user does not need to formulate a query, this is implicit and corresponds to the resources that match the user's interests at the right time. However, despite the relevance of the personalized information delivered to the user, he/she may choose to reject recommendations in certain situations. This abstinence may not concern the recommended information itself but it takes part in the situation the user may be in and during which the user does not want to be disturbed. Thus, it is important to include the risk of disturbing the user within the recommendation process. This is called risk-aware recommendation. The works proposed in [START_REF] Lin | Indoor location-based recommender system[END_REF], [START_REF] Melguizo | What a proactive recommendation system needs -relevance, non-intrusiveness, and a new long-term memory[END_REF], [START_REF] Pu | Location based recommendation for mobile users using language model and skyline query[END_REF], [START_REF] Quercia | Nurturing social networks using mobile phones[END_REF] considered this aspect from a user modelling perspective and considered that intrusiveness is limited to figuring out implicitly the user's preferences and related information. As it comes to the works [START_REF] Bouneffouf | A contextualbandit algorithm for mobile context-aware recommender system[END_REF], [START_REF] Bedi | A situation-aware proactive recommender system[END_REF] that integrated intrusiveness into the recommendation process, they only relied on the user's agenda activities to assess if they can send a recommendation or not. In this paper, we propose an approach for assessing intrusiveness within a proactive recommendation approach, not only in terms of the user's agenda activities but also including the user's context with its several level of representation and other applications embedded in the user's mobile device. Indeed, we believe that this kind of contextual information could help understand the situations in which recommendations are subject to rejection. The main contributions of this paper can be summarized as follows:

• A model for intrusiveness assessment within the recommendation process that makes use of the applications and the sensors embedded within the user's mobile device along with contextual information. • An extensive user study evaluation for intrusive recommendation assessment.

To our knowledge, there is no existing empirical research that addresses intrusive recommendation in a mobile environment the way we tackle it in this paper. The paper is organised as follows. We provide in section 2 a literature review about proactive recommender systems and risk-aware recommendation. Section 3 details the proposed approach. We describe in section 4 the experiments conducted and we finish in section 5 with a conclusion and thoughts for future work.

II. RELATED WORK We present, in this section, an overview on the proactive recommendation domain and the concepts that it entails. We also introduce the intrusiveness aspect and how it was addressed in literature.

A. Proactive recommender systems

Proactive Recommendation Systems (PRSs) as described by [START_REF] Melguizo | What a proactive recommendation system needs -relevance, non-intrusiveness, and a new long-term memory[END_REF], retrieve large quantities of documents, decide what available information is most likely relevant to the user needs, and offer that information without the user's request. Ricci [START_REF] Ricci | Mobile recommender systems[END_REF] considers that proactive recommender systems "can revolutionize the role of RSs from topic oriented information seeking and decision making tools to information discovery and entertaining companions". The use of contextual information, particularly, in a mobile environment, is very crucial to boost the performance of such systems. The concept of context has been addressed in many works and has been defined through different aspects [START_REF] Schilit | Disseminating active map information to mobile hosts[END_REF], [START_REF] Brown | Context-aware applications: from the laboratory to the marketplace[END_REF], [START_REF] Ryan | Enhanced reality fieldwork: the context aware archaeological assistant[END_REF], [START_REF] Dey | Context-aware computing: The cyberdesk project[END_REF], [START_REF] Allen | Information needs: A person-in-situation approach[END_REF], [START_REF] Sonnenwald | Exploring the contexts of information behaviour[END_REF], [START_REF] Cool | The concept of situation in information science[END_REF]. The most commonly and widely used definition for context presented it as the cognitive, the social and the professional environment which cover situations related to factors such as location, time and the current application [START_REF] Lawrence | Context in web search[END_REF], [START_REF] Quiroga | An experiment in building profiles in information filtering: The role of context of user relevance feedback[END_REF], [START_REF] Ingwersen | Information retrieval in context: Irix[END_REF], [START_REF] Bottraud | Expansion de requetes par apprentissage automatique dans un assistant pour la recherche d'information[END_REF]. There are several approaches that used location as an approximation of context. The Global Positioning System (GPS) integrated or installed in the device helps to define the user's location. This location is displayed, according to latitude and longitude. Those GPS coordinates are not the only features that we can consider when defining a location. Indeed, as discussed by [START_REF] Dobson | Leveraging the subtleties of location[END_REF], there are different ways to characterize the location of the mobile user :

• Absolute position • Relative (next to, ... )

• A Place name • A named class that represents the type of the place, eg. museum, school, ... . The place type can also be recovered using a Geographical Information System (GIS) such as geonames 4 or foursquare 5 which assigns a location category (restaurant, train station, etc.) to a given GPS coordinates. The localization accuracy helps to determine the user's context in a more precise manner [START_REF] Christoph | Context detection on mobile devices[END_REF]. Time was also used as a context dimension that helps to boost the recommendation relevance. It may be represented as a continuous variable whose values determines the specific time period at which items are rated by a given user. Example: user A rated item I at t = June 1st, 2010 at 18:05:00 Another way to model time is to identify categorical values, for the time periods of interest. For example, in the tourism domain, the variable "season" can be expressed as: season = {hot season, cold season}. 4 http://www.geonames.org/ 5 https://www.foursquare.com/ Time can also be modelled in a hierarchical way which makes possible to define the degree of granularity of the time context information. Example:

W eek Day = {Monday, Tuesday, . . . , Sunday} ⇒ time = {morning, afternoon, ..., night} The user's activity may also be used as a contextual information within the recommendation process. Chen et al. [START_REF] Chen | A survey of context-aware mobile computing research[END_REF] described the user's activity through three different schemes:

• Machine vision: using image processing and camera technology • The user's calendar: to figure out the different activities scheduled at a certain time • Artificial Intelligence techniques: that help to determine contextual information by leveraging low-level sensors. The user activity may be determined from the different application and sensors installed in the mobile device such as the camera, the accelerometer, or the microphone. The data provided by the sensors can be saved in context logs in the device or sent to the server. All of these contextual information describe user intentions and needs and constitute important factors for relevant proactive recommendations. Indeed, most of the systems that have been developed for proactive recommendation relied on contextual triggers to initiate the recommendation process. These systems can be partitioned into the following typology.

1) Spatio-Temporal based systems: The recommender systems that rely mainly on the spatio-temporal factors focus generally on a specific domain like tourism or restaurants recommendation. Opperman et al. [START_REF] Oppermann | A context-sensitive nomadic exhibition guide[END_REF] developed a system called HIPPIE that proactively recommend to users upcoming events and exhibits within a tourist user guide using indoor positioning technologies and maps. The work presented in [START_REF] Braunhofer | A context-aware model for proactive recommender systems in the tourism domain[END_REF], proposed a proactive recommender system for points of interests (POI) employing mainly time and the user's visiting history of POI. The latter factor was also used in [START_REF] Li | Want a coffee?: Predicting users' trails[END_REF] within a Markov chain model to predict the user's next visits. Vico et al. [START_REF] Vico | A study on proactive delivery of restaurant recommendations for android smartphones[END_REF] made use of other contextual factors like the social dimension (user alone or accompanied), besides the temporal and the geographical aspects, to proactively recommend restaurants to a user. Tong et al. [START_REF] Tong | Time and location-based hybrid recommendation system[END_REF] proposed a proactive approach for next purchase basket recommendation. They considered this approach as a binary classification problem (buy or not) in which features are mainly based on time and location.

2) The user's current or past behaviour based systems: In order to proactively recommend items to users, various approaches depend on various factors, to mention :

• The user's past or actual behavior history that includes for example previous visiting behaviors for location based systems [START_REF] Li | Want a coffee?: Predicting users' trails[END_REF], [START_REF] Pu | Location based recommendation for mobile users using language model and skyline query[END_REF]; • Web browsing history/clicks [START_REF] Melguizo | What a proactive recommendation system needs -relevance, non-intrusiveness, and a new long-term memory[END_REF] • Previous reading patterns for news recommender systems [START_REF] Ijntema | Ontologybased news recommendation[END_REF], [START_REF] Athalye | Recommendation system for news reader[END_REF], [START_REF] Dumitrescu | Improving novelty in streaming recommendation using a context model[END_REF] Sae-Ueng et al. [START_REF] Sae-Ueng | Personalized shopping assistance service at ubiquitous shop space[END_REF] analysed the user's behavior log for shopping assistance using a digital camera and RFID sensors 6 . The system recommended information about a product according to the user's behavior classified under five states: Standing, Viewing, Touching, Carrying, and Fitting. Elbery et al. [START_REF] Elbery | Proactive and reactive carpooling recommendation system based on spatiotemporal and geosocial data[END_REF] developed a carpooling recommender system that makes use of the user's past visiting history and information collected from the user's social networks accounts. The information collected is then used in a time markov chain. In [START_REF] Ayachi | Proactive and reactive e-government services recommendation[END_REF], the authors used the users' behaviour patterns extracted from social networks to develop a personalized recommender system for e-government services.

3) Activity-centric systems: Other approaches considered recommendation triggers from an activity centric angle. The triggers might take the form of:

• Ongoing conversation or activity such as text messages, phone calls [START_REF] Popescu-Belis | A speechbased just-in-time retrieval system using semantic search[END_REF] • Opened web pages or documents [START_REF] Prekop | Activities, context and ubiquitous computing[END_REF], [START_REF] Dumais | Implicit queries (iq) for contextualized search[END_REF], [START_REF] Karkali | Match the news: A firefox extension for real-time news recommendation[END_REF] • The social media activity of the user such as the content of the user's tweet stream on Twitter7 [START_REF] De Francisci Morales | From chatter to headlines: Harnessing the real-time web for personalized news recommendation[END_REF], [START_REF] O'banion | Social media-driven news personalization[END_REF] Morales et al. [START_REF] De Francisci Morales | From chatter to headlines: Harnessing the real-time web for personalized news recommendation[END_REF] developed a recommendation approach to suggest interesting news to users by exploiting the information in their twitter persona. They model relevance between users and news articles using a mix of signals drawn from the news stream and from twitter. This latter is used to build the profile of the social neighbourhood of the users, the topic popularity in the news and the content of their own tweet stream. They showed that the combination of microblogging platforms and real-time web signals can be interesting triggers to send notifications to users. Phelan et al. [START_REF] Phelan | On using the realtime web for news recommendation & discovery[END_REF] presented a news recommendation system named Buzzer, which is able to send recommendations about articles according to the conversations that are taking place on Twitter. The system uses a content-based approach by mining trending terms from both the public Twitter timeline and from the timeline of tweets generated by a user's own social graph (friends and followers). The system also looks for terms co-occurrences within the tweets and the RSS articles. Therefore,during recommendation, the articles that match the recent Twitter content will be recommended.

B. Risk-aware recommendation

The Cambridge Dictionary8 defines intrusiveness as an act:

"Affecting someone in a way that annoys them and makes them feel uncomfortable." Intrusiveness was also defined in [START_REF] Li | Measuring the intrusiveness of advertisements: Scale development and validation[END_REF] as :

"A perception of psychological consequence that occurs when an audience's cognitive processes are interrupted." The intrusiveness concept was tackled within different applications that attempted to put forward an approach for detecting it. In [START_REF] Hinckley | Toward more sensitive mobile phones[END_REF], intrusiveness or interruptibility as the authors preferred to call it, is measured using the likelihood of the user to respond to phone calls computed using sensors embedded within the user's mobile device. These sensors were able to detect the user's proximity regarding the device :

The user holds the device The device is close to the user's head Siewiorek et al. [START_REF] Siewiorek | Sensay: A context-aware mobile phone[END_REF] engineered an application that adjusts the device ring tone according to the user's surroundings inferred from the microphone, the light and the accelerometer. Alcala et al. [START_REF] Alcalá | Assessing human activity in elderly people using non-intrusive load monitoring[END_REF] proposed a non-intrusive application for monitoring human activity for health care using a smart meter data.

The system is able to collect data implicitly, monitors the user's behaviour pattern and sends notifications when it detects behaviour anomalies. In [START_REF] Liu | A personal, mobile system for understanding stress and interruptions[END_REF], intrusiveness is perceived as an interruption that should be avoided when a user is in a particular emotional state which is determined by a pedometer and a heart rate monitor. The authors assumed that the user's should not be interrupted or disturbed when the system detects that he/she is "stressed" or "angry". When it comes to the recommender system domain, intrusiveness was considered in [START_REF] Bouneffouf | A contextualbandit algorithm for mobile context-aware recommender system[END_REF] as a risk of disturbing the user and was defined as:

"the possibility to disturb or to upset the user which leads to a bad answer of the user". Several works addressed this aspect as a user modelling issue and considered that a non-intrusive recommendation approach is an approach that can implicitly figure out the users' preferences and related information [START_REF] Adomavicius | Multi-criteria recommender systems[END_REF], [START_REF] Farinella | A non-intrusive movie recommendation system[END_REF], [START_REF] Palanivel | A study on implicit feedback in multicriteria e-commerce recommender system[END_REF]. In the following sections, we present the two types of approaches that addressed intrusiveness within RSs.

1) Non-intrusiveness as implicit user profiling: This concept was considered by several works from an implicit user profiling perspective assuming that non intrusiveness is keeping track of the user's preferences implicitly. Lin [START_REF] Lin | Indoor location-based recommender system[END_REF] described the recommender system he proposed as non-intrusive as it estimates the user's preferences from the time the user spends in a shop without explicitely asking the user. Melguizo et al. [START_REF] Melguizo | What a proactive recommendation system needs -relevance, non-intrusiveness, and a new long-term memory[END_REF] used the text that was currently written by the user to recommend items that are relevant to the text that was written. They perceive this kind of recommendation approach as proactive and non-intrusive as it supports authors in the writing task without asking for their involvement. Pu et al. [START_REF] Pu | Location based recommendation for mobile users using language model and skyline query[END_REF] designed a location based recommendation system to provide the most possible interesting places to a user according to her/his implicit preference and physical moving location without the user providing her/his preference or a query explicitly. They proposed two circle concepts, physical position circle that represents spatial area around the user and virtual preference circle that is a non-spatial area related to the user's interests extracted from her/his historical visiting behaviour. Quercia et al. [START_REF] Quercia | Nurturing social networks using mobile phones[END_REF] proposed a system that automatically recommends new friends, tracks the health of friendships and gains awareness of the user's mood by monitoring implicitly his/her activity with mobile phones including monitoring text messages, phone calls and encounters captured by Bluetooth. They have engineered a new technology for mobile phones that silently keeps track of people's colocation, as well as frequency of voice calls and text messages. They also explored the degree to which the engine can predict users' moods (e.g., happiness, sadness) simply based on their activity. The proposed framework called FriendSensing enables new members of social-networking websites to automatically discover their friends. It also helps existing members to elicit new social relations, as they develop over time. These services uses short-range radio technologies (e.g., Bluetooth) for logging encounters and rely on colocation records to elicit relevant encounters and to arrange them into a weighted social network for recommending friends. Unfortunately, the above mentioned works and several others dealt with the intrusiveness concept as a matter of retrieving the user's interests and not regarding the fact that the recommendation itself may disturb the user. Indeed, intrusiveness can also be an issue within the recommendation process and not only in the user's modelling process.

2) Non-intrusiveness as non-disturbing recommendation: The work presented in [START_REF] Bouneffouf | A contextualbandit algorithm for mobile context-aware recommender system[END_REF] was the first to integrate intrusiveness as a phase in which they assess the risk of disturbing the user before recommending. They consider a situation as a triplet composed of location, time and the user's agenda activity. They define a "critical" or "risky" situation as a situation in which a user does not want to be disturbed. For each situation, they compute a risk score that depends on the risk-level of the concept describing the user's activity extracted from his/her agenda. They assume that a situation is deemed risky if its risk score exceeds a pre-defined threshold. This approach is specifically designed for employees working at a company and do not consider other type of users. Bedi et al. [START_REF] Bedi | A situation-aware proactive recommender system[END_REF] integrated a situation assessment phase in their approach for recommending restaurants, in which they use fuzzy logic as an inference technique that depends on distance, time, budget and reachability, to assess the non-intrusiveness level (also called the context level) of a given situation. They predefine the fuzzy sets for the context level and for the attribute Distance as follows :

Distance={Near,Moderate,Far} Context-level={Low,Medium,High}
The link between the context attributes and the context-level is represented as rules; which means that the context level is inferred depending on the values of the attributes. The work proposed in [START_REF] Betzalel | Please, not now!: A model for timing recommendations[END_REF] considered intrusiveness in a recommendation approach as a classification problem which aims at identifying whether a given context is "good" or "bad" to trigger the recommendation process. They collected mobile data over a three weeks user study in order to learn the classification model. To sum up, even when some works tried to deal with the intrusiveness issue, they always tend to look at the surroundings of the user forgetting that the big amount of applications embedded in the user's phone could be the issue itself and can help figure out if recommending in a given situation is appropriate or not. Thus, in this paper, we propose to assess intrusiveness not only in terms of context as generally defined by time and location, but also considering the applications that a user is using at a given situation.

III. MEASURING INTRUSIVENESS FOR PROACTIVE

RECOMMENDATION

We propose to integrate an intrusiveness assessment phase into a context-aware proactive recommendation approach that covers multiple domains [START_REF] Akermi | Just-in-time recommendation approach within a mobile context[END_REF]. It aims at recommending relevant items that match a user's situation without waiting for the user to initiate any interaction. The recommendation process is not launched until we assess the intrusiveness level of the situation. We consider that the user's daily routine is represented as a pack of situations that reflects a specific category of interest described by the the spatio-temporal dimensions' instantiations and the user's actual activity. A situation is characterized by four dimensions: time of the day, the weekday, the actual location and the user's activity presented respectively as: S = (D t , D w , D l , D a ). To overcome the cold start problem when first using the recommender system, we predefine for typical situations, a particular category of information C to recommend (Restaurants, News, Traffic information, etc). For example, the situation "Lunch time" is typically described by : D l : At work; D w : Monday; D t : t ∈ [12 : 00, 14 : 00]; D a : the user is taking a break

For such situation, the category of information to recommend and that suits the best is "Restaurant". Therefore, we consider that a situation, with its different levels of representation, defines the changing user's need in information.

Then, the type of information needed for an actual user situation is updated according to the user's feedbacks to past situations. In this paper, regardless of the type of information to recommend in a given situation (that we addressed in a previous article [START_REF] Akermi | Just-in-time recommendation approach within a mobile context[END_REF]), we propose an approach for balancing the process of recommendation against intrusive interruptions. Indeed, there are different factors that make the user less open to recommendations and as we are working within the framework of mobile devices, we consider that the several embedded applications in a mobile phone such as the camera, the keyboard, the accelerometer, agenda, etc. are good representatives of the user's interaction with her/his device since they somehow stand for the most undertaken activities in a mobile device such as texting messages, chatting, tweeting, browsing or taking selfies and pictures. Indeed, according to a recent study 9 , 85% of smartphone users spend more than 4 hours a day texting, surfing, talking and tweeting. Besides, 90% of the people surveyed reported using their smartphones to take pictures at least once a week. Thus, we believe that we should take into account the applications that are enabled at a given situation to figure out the user's activity. We opted for a case-based reasoning approach based on the analogous use of past cases to figure out if we could interrupt the user's current activity and send a recommendation. A user's past case is modelled as case(premise, value):

• PREMISE : describes the situation S i = {week day, time of the day, current activity} described by the instantiated dimensions that it entails. The premise is used to measure the similarity between the cases • VALUE : integrates 2 parameters, value(nb y , nb n ) that refer respectively to the number of times the user agreed to receive a recommendation at situation S i and the number of times the user rejected the recommendation at situation S i without reading the content of the recommendation. This is to ensure that the rejection is not induced by the recommended content but rather by the user's situation.

Let S c be the current user's situation and S the set of past situations stored in the recommendation feedback database.

The system compares S c with the situations in S in order to figure out the feedback that was given to a similar situation S p :

S * = argmax Sp∈S sim(S c , S p ) (1)
The following sections detail the different phases of the intrusiveness assessment process:

• Retrieval: describes the matching process between the actual situation and the past ones. • Re-use: examines the retrieved past situation's feedback in order to figure out the user's eventual response for a given recommendation. • Revision: recovers the current user's feedback regarding a recommendation.

A. Retrieval

The similarity between two situations takes into account the similarities between the situations' features:

sim(S c , S p ) = i α i sim(F i c , F i p ) (2)
9 https://www.comscore.com/Insights/Blog/Mobile-Matures-as-the-Cross-Platform-Era-Emerges Where : F i c represents the i th feature of the situation vector S c (respectively S p ) and i α i = 1.

The following sections show how we calculate the respective similarities.

1) Time feature similarity: The similarity of the time feature takes into account two levels : time of the day and the week day:

• The week day Assuming that the user lives in a Western country, the weekdays can be partitioned as following: D w ∈ {work days{monday, ..., f riday}, rest days{saturday, sunday, public holiday}} This partition is automatically changed according to the user's location. Indeed, while Saturdays and Sundays may be rest days in most Western countries, this is not the case for Middle-Eastern countries, where Friday is typically a rest day and Sunday is not. We sequentially enumerate the week days (1 for monday, ..., 7 for sunday) in order to compute the similarity between two week days in terms of proximity as:

sim(D c w , D p w ) = 1 - |D c w -D p w | nd (3)
Where nd stands for the number of the week days, which is 7.

• Time of the day

We choose to divide a daily routine into four periods (morning, midday, afternoon and evening) that are framed within 24 hours intervals. D t ∈ {morning[07 : 00, 12 : 00], midday[12 : 00, 14 : 00], af ternoon[14 : 00, 18 : 00], evening[18 : 00, 00 : 00]} In order to calculate the similarity between two time intervals, we rank each period from 1 (morning) to 4 (evening):

sim(D c t , D p t ) = 1 - |D c t -D p t | np (4)
Where np stands for the number of the time periods defined, which is 4.

2) The user's activity similarity: At a given situation S, the system takes a snapshot of the user's current activity A c by checking the agenda activities and the current enabled application such as driving, texting messages, tweeting or browsing, using the sensors and the applications embedded in the user's mobile device. For example, we can figure out if the user is in a meeting according to his agenda or if the user is taking a picture by checking if the camera is enabled or not. Thus the similarity computation of the user's activity related to two situations is computed as:

sim(A c c , A p c ) =    1 if A c c = A p c 2×d3 d1+d2+2×d3 else (5)
In order to overcome the drawback of syntactic similarity (perfectly matching words or phrases) [START_REF] Ferreira | Assessing sentence similarity through lexical, syntactic and semantic analysis[END_REF], we compute the Wu and Palmer [START_REF] Wu | Verbs semantics and lexical selection[END_REF] semantic similarity of the two activities defined as the shortest path between two concepts within the Wordnet 10 lexical graph, where:

d1 is the depth of A c c d2 is the depth of A p c
d3 is the depth of the least common subsumer (LSC) which stands for the closest ancestor concept to the two activities.

B. Re-use

Once we retrieve the most similar situation to the current one, we use the past user's feedback in order to decide whether we should send a recommendation or not. If, for the similar situation, the number of times the user disregarded the notification (nb n ) exceeds the number of times the user agreed to receive a notification (nb y ), we would take that as a "do not disturb me" feedback. We also consider the coldstart problem that arises when there is no similar situation among the past ones. When this occurs, we assume that the recommendation will not disturb the user and that we can push the recommendation into the user's device screen.

C. Revise

The revision phase consists of recovering the user's feedback regarding the recommendation related to the current situation. The user's click on the recommended information is considered as a "POSITIVE" feedback, meaning that the notification did not disturb the user. If the user chose to disregard the recommendation by swiping the notification displayed on the device's screen, we take that as a "NEGATIVE" feedback. The new feedback of the actual situation may serve for the construction of a new case or the update of an existing one depending on the similarity score that was previously computed. If the similarity score between a past case and the user's actual situation exceeds a threshold λ = 0, 6 (indicating that the two situations have at least two features' values that match perfectly), we accordingly update nb y or nb n within the value section (i.e. feedback) of the similar situation. Otherwise, the current case along with its actual feedback will be added to the case base:

Algorithm 1: The revision process if (Sim(S c , S p ) ≥ λ) then if f eedback Sp = "P OSIT IV E" then nb Sp y = nb Sp y + 1 else nb Sp n = nb Sp n + 1 end if else
Add S c to the case base end if 10 https://wordnet.princeton.edu/ IV. EXPERIMENTS Since there is not a suitable dataset to experiment the approach we propose, we conducted a user study. Indeed, user studies [START_REF] Ekstrand | User perception of differences in recommender algorithms[END_REF] are good alternatives for evaluating recommender systems in which users are asked to evaluate recommendations. This kind of evaluation allows a subjective assessment of the system as surveys can be conducted along with the experiments.

A. User Study

We automatically generated situations that simulate real life situations the user might be in and that are characterized by four features : the week day, time of the day, the current activity the user might be doing and the category of information that might be recommended (News, POI, restaurant, gift idea, TV program, etc.). The possible values of the first three features were determined using a survey conducted within the IRIT lab 11 with colleagues from different backgrounds and age range. The values set of the fourth feature was addressed in [START_REF] Akermi | Just-in-time recommendation approach within a mobile context[END_REF]. We filtered the set of situations generated to take out those that are not likely to happen, for example, having a meeting at late night at home. We also made sure that the values gathered for the current activity feature cover most of the activities that can be inferred from the applications and sensors installed within the device. We settled for 100 situations. Users were asked, given a situation they might be in, if they accept to get a recommendation or not. They were also asked to mention if they consider the information type (News, POI, ...) recommended at that situation as relevant or not. They also had the possibility to comment on every situation. For example, a situation can be described to the user as:

It is Saturday,Midday and you are doing the following activity : Taking a picture/selfie Would you accept to get a notification :

• YES • NO Given this situation, do you think that recommending Restaurants is interesting :

• YES • NO Comments: (Please comment your answers)
We used the crowdflower 12 platform to run the user study. In order to avoid any bias, we configured several quality control mechanisms such as speed traps which measure the time spent by a participant to answer the questions of the study. We also made sure that the participants understand perfectly English and the question they were asked. Figure 1 gives an overview about the conducted user study.

B. Results

The purpose of this study was to gather real users' judgements about situations that might occur in real life. Thus, after parsing the collected data, we got about 1500 users who participated to this study. In this paper, we only considered the first section of this study addressing the issue of accepting to receive a recommendation or not, regardless of its content. The approach used to tackle the second section, that consideres the type of information to recommend automatically according to the user's situation, is presented in [START_REF] Akermi | Just-in-time recommendation approach within a mobile context[END_REF]. In order to determine the accuracy of the approach in terms of intrusiveness assessment, we consider a cross-validation evaluation that estimates the reliability of a model based on a sampling technique. We run a K-fold cross-validation test (K = 10) that consists of partitioning, for each user, a sample data that is used as a training set and then use the remaining data for testing. This process is repeated for each user K times. Then, we calculated the Mean Average Precision (MAP) for every possible feature combination and for the two baselines.

M AP = U u=1 AveP (u) U (6) AveP (u) K k=1 rel s S K (7)
Where :

U is the number of users rel s is the number of correctly assessed situations for each run K is the number of runs (K = 10) S is the number of situations Figure 3 illustrates the obtained results. As shown in Figure 2, the proposed approach, using all the features equally distributed (α = 1, see Eq. 2), scores a MAP of 87% against 64% for Baseline A that always sends recommendations without considering the user's interruptibility and 50.81% for Baseline B that consists in not sending a recommendation when an application is ON. We also note that the combinations that entail the activity feature, like Activity-Day, Activity-Time and Activity, score a high precision.

Even when we varied the weights α i assigned to each feature, the user's current activity still takes over the other features to determine the intrusiveness level of a situation (see table I). Then, we can consider that the activity feature is a discriminative attribute for deciding whether a situation is conducive to receive a recommendation or not. α activity α time α day precision 0,0 0,0 1,0 0,65 0,0 0,2 0,8 0,62 0,0 0,5 0,5 0,62 0,0 0,7 0,3 0,63 0,0 1,0 0,0 0,67 0,2 0.0 0,8 0,71 0,2 0,2 0,6 0,74 0,2 0,5 0,3 0,75 0,2 0,7 0,1 0,73 0,5 0,0 0,5 0,84 0,5 0,2 0,3 0,85 0,5 0,5 0,0 0,84 0,7 0,0 0,3 0,84 0,7 0,2 0,1 0,86

As we explained earlier, the category of information to recommend (News, coffee shop, POI, ...) is inferred according to the user's situation [START_REF] Akermi | Just-in-time recommendation approach within a mobile context[END_REF]. Therefore, we also used this study to put forward the topical relevance of the recommended information regarding the situations that were proposed. For each situation, we measured the average precision score computed as the proportion of users who rated the recommended information, according to the given situation, as relevant:

AveP (u) S s=1 nb u rel U S ( 8
)
Where S is the number of situations, nb u rel is the number of users who judged a situation s as relevant and U is the total number of users.

The approach scored 85% for topic relevance accuracy with an inter-agreement coefficient equal to 0,76.

C. Analysis

The performance of Baseline B, which considers that a recommendation should not be sent when an application is ON, proves that approaches that automatically consider the use of any random application at a given situation as a hinder to sending a recommendation, are not effective. It actually depends on the type the application being used and on the user's behaviour. Indeed, given the user study data, we analysed the users' responses and behaviours regarding recommendations according to time and activity. As shown in Figure 3, we computed the proportion of users who considered recommendations, in certain activities, as annoying or not. We only put forward the 5 most used applications in a mobile device. We note that more than 70% of the participants accepted to receive recommendations when tweeting or chatting. This could be explained by the fact that people may want to share with others the recommended information.We also notice that 59% of the participants against 41% were not disturbed when getting a recommendation while taking a picture which could be somehow interpreted as senseless because we normally expect users to get annoyed if they were interrupted while typing a message or using the device's camera. Actually, it depends on the user's preference and behaviour pattern. That is why the case-based reasoning approach we propose to address the intrusiveness aspect is revealed to be efficient since it considers every user apart and does not follow a typical trend. We also studied the user acceptance regarding receiving notifications according to the time and day of the week. As expected and as illustrated by Figure 4, the two most important peaks to observe happen during breaks and after work. Indeed, it is during these two periods of the day that people have more spare time to spend for activities other than work and chores. The user study that we conducted entails a lot of information that can be used for recommender system's evaluation. Indeed, we made this user study available 13 for the RS research community as a dataset for proactive and context-aware RS evaluation. This can help alleviate the datasets shortage and provide a framework for different approaches to be compared on a same basis. The purpose of this study is to investigate, considering a user's situation, whether any recommendation should be sent at all, regardless of its content. However, we believe that the content is still important to determine whether a recommended item is disturbing or not. For example, a user may not want to be disturbed usually when working but perhaps work related news is still acceptable. Therefore, we are currently working on integrating into the approach we proposed, a trade-off between the importance of the information to be recommended and the risk of disturbing the user. Indeed, in some situations, even though the user chose not to be disturbed, the recommended information might be worth being interrupted for, such as breaking news or an accident that happened on the user's way home. We believe that such trade-off needs to be studied.

V. APPLICATION SCENARIO

The recommendation approach that we propose has been developed by an IT company 14 within the framework of a project funded by the European Union. The Implemented application is a proactive and non-intrusive recommender system that enables users to get relevant recommendations according to their current situations. The application covers multiple domain item recommendation and is tailored to the users' preferences extracted from their Facebook accounts [START_REF] Akermi | Using Social Networks for Mobile Proactive Recommendation[END_REF] and from their behaviour pattern (browsing history and clicks on the recommended items). The mobile application is developed within a client/server model and it is deployed on the server part. The user only gets the visible and the interactive parts of the application on his/her mobile device. According to a time trigger installed on the user's device, an implicit request including contextual information is sent to the server to be analysed in order to assess the user's situation for intrusiveness and launch the recommendation of the appropriate information [START_REF] Akermi | Just-in-time recommendation approach within a mobile context[END_REF]. The following figures gives an overview about the application (the language used in the application is french as it is going to be launched in a french speaking market). The link provided allows the user to log into his/her facebook account in order to create his/her user profile for a personalized application usage. Once the user gives permission to access his/her likes, the application collects the required information. As we explained in the previous sections, according to given situations, the application implicitly initiate the recommendation process after assessing the user's interruptibility and displays a notification icon entailing a brief description about the recommended information in the notification bar of the mobile device. The main purpose of the application is to provide proactive information to the user without disturbing her/him, besides allowing to check for information manually in case the user does not want to wait for an implicit and proactive recommendation (see figure 8).The user has also the possibility of switching on/off notifications about a given information category. Information gathering and the evaluation of this application on a real life basis, regarding intrusiveness assessment is still ongoing.

VI. CONCLUSION

In this paper, we introduced an approach for assessing intrusiveness within a proactive recommendation approach. The approach entails a case-based reasoning process that makes use of the user's surroundings and the applications embedded within the user's mobile device in order to assess intrusiveness before recommending. The experiments that we have conducted using a user study yielded promising results. Besides we constructed an evaluation framework based on a user study that we made available for the scientific community and that can be used to assess context-aware proactive recommender systems effectiveness.

  IS 'Near') AND (Time IS 'In-Time') AND (Budget IS 'Affordable') AND (Reachability IS 'High') THEN Context-level IS 'High'

Fig. 1 .

 1 Fig. 1. The user study overview

Fig. 2 .

 2 Fig. 2. The recommendation accuracy using intrusiveness assessment

Fig. 3 .

 3 Fig. 3. The users' behaviour regarding some activities

Fig. 4 .

 4 Fig. 4. The notification acceptance rate according to the time of the day

Fig. 5 .

 5 Fig. 5. The notification acceptance rate according to the day of the week People tend to be more receptive to suggestions at the weekend.The user study that we conducted entails a lot of information that can be used for recommender system's evaluation. Indeed, we made this user study available13 for the RS research community as a dataset for proactive and context-aware RS evaluation. This can help alleviate the datasets shortage and provide a framework for different approaches to be compared on a same basis. The purpose of this study is to investigate, considering a user's situation, whether any recommendation should be sent at all, regardless of its content. However, we believe that the content is still important to determine whether a recommended item is disturbing or not. For example, a user may not want to be disturbed usually when working but perhaps work related news is still acceptable.

Fig. 6 .

 6 Fig. 6. Connecting on Facebook

Fig. 7 .

 7 Fig. 7. Notification

Netflix https://www.netflix.com/

Tripadvisor https://www.tripadvisor.com/

Youtube https://www.youtube.com/

https://en.wikipedia.org/wiki/Radiofrequency identification

https://twitter.com/

http://dictionary.cambridge.org/

https://www.irit.fr/

https://www.crowdflower.com/

http://www.pasri.tn/

http://www.tunav.com/fr

ACKNOWLEDGEMENT

The authors would like to thank the European Union program PASRI 15 and Mr. Aymen ELJ, the Research and Development department manager in TUNAV 16 , for hosting this research and for his valuable support.