Elena Viorica Epure
email: elena.epure@malix.univ-paris1.fr

Benjamin Kille
email: benjamin.kille@dai-labor.de

Jon Espen Ingvaldsen

Rebecca Deneckere
email: rebecca.deneckere@univ-paris1.fr

Camille Salinesi
email: camille.salinesi@univ-paris1.fr

Sahin Albayrak
email: sahin.albayrak@dai-labor.de

Recommending Personalized News in Short User Sessions

Keywords: Information systems → Data analytics, Content ranking, Web log analysis, Data stream mining, Personalization, News reading behavior, News reading interests, Recommender system, Personalization, Markov processes, Stationarity analysis

News organizations employ personalized recommenders to target news articles to speci c readers and thus foster engagement. Existing approaches rely on extensive user pro les. However frequently possible, readers rarely authenticate themselves on news publishers' websites. is paper proposes an approach for such cases. It provides a basic degree of personalization while complying with the key characteristics of news recommendation including news popularity, recency, and the dynamics of reading behavior. We extend existing research on the dynamics of news reading behavior by focusing both on the progress of reading interests over time and their relations. Reading interests are considered in three levels: short-, medium-, and long-term. Combinations of these are evaluated in terms of added value to the recommendation's performance and ensured news variety. Experiments with 17-month worth of logs from a German news publisher show that most frequent relations between news reading interests are constant in time but their probabilities change. Recommendations based on combined shortterm and long-term interests result in increased accuracy while recommendations based on combined short-term and medium-term interests yield higher news variety.

INTRODUCTION

e digital transformation has been a game changer for legacy news media. Keeping readers loyal has become highly competitive for publishers as their new business models rely on advertisement or related revenues [START_REF] Newman | Overview and Key Findings of the 2016 Report[END_REF]. Personalized recommendation has emerged as a popular way to tackle this challenge by automatically suggesting news to online readers. However, creating e ective solutions faces particular constraints [START_REF] Atle Gulla | ird International Workshop on News Recommendation and Analytics (INRA 2015)[END_REF][START_REF] Lommatzsch | Real-Time News Recommendation Using Context-Aware Ensembles[END_REF]. Large publishers release hundreds of news daily, implying that they must deal with fast-growing numbers of items that get quickly outdated and irrelevant to most readers. News readers exhibit more unstable consumption behavior than users in other domains such as entertainment. External events a ect, e.g. breaking news, a ect readers interests [START_REF] Atle Gulla | ird International Workshop on News Recommendation and Analytics (INRA 2015)[END_REF]. In addition, the news domains experiences extreme levels of sparsity.

Existing news recommender solutions address these challenges to some extent. Most of them suggest fresh and popular news. Some consider the dynamics of news reading behavior. However, they rely on the availability of rich user pro les for personalized recommendations. Still, many publishers lack this knowledge, unlike news aggregators' services [START_REF] Ahmed | Fair and Balanced : Learning to Present News Stories[END_REF][START_REF] Das | Google News Personalization: Scalable Online[END_REF] or company blogs [START_REF] Sahoo | A Hidden Markov Model for Collaborative Filtering[END_REF], as readers tend to consume online news without authentication. Cross-device and cross-browser tracking are techniclly challenging endevaors. us, in practice, publishers observe relatively short sessions with fewer than ten clicks on average [START_REF] Doychev | An Analysis of Recommender Algorithms for Online News[END_REF][START_REF] Epure | Process Mining for Recommender Strategies Support in News Media[END_REF][START_REF] Lommatzsch | Real-Time News Recommendation Using Context-Aware Ensembles[END_REF].

As per-user models are unsuitable in short sessions, we propose a new approach to recommendation which ensures basic personalization. e approach combines crowd reading behavior over time and the current user session. ereby, we model crowd reading behavior for di erent time frames. When providing recommendations, should the models from the same day, from the last weeks, or from the last months be used? In the current work, we analyze the dynamics of the crowd news reading behavior and its e ect on recommendations. We show that such design choices can signi cantly a ect the outcome.

is work includes four contributions. First, we extend the existing distinction between short-and long-term reading behavior as we establish medium-term reading behavior. e reading behavior concerns readers' interests, which are linked to news categories. Section 5.1 conveys a detailed description. Second, we identify reading episodes and derive models speci cally re ecting engage reading. ird, we assess the dynamics of the crowd reading behavior, complementary to related studies in news media. Speci cally, we focus on the evolution of the relations between news categories rather than on standalone categories. Fourth, we compare three recommendation policies in terms of performance and news variety: (a) a policy based merely on short-term behavior; (b) a policy integrating short-and medium-term behaviors; (c) a policy integrating short-and long-term behaviors. Experiments are conducted with real data from a German news publisher, spanning 17 months. e long period is chosen to account for uctuations caused by seasonal or other types of e ects.

Our ndings show that in reading episodes users are likely to read news within the same category. e relations between news categories are stable in time as the most likely target categories from a given source stay the same. However, their priorities, represented by transition probabilities, change every 1 to 4 months. Augmenting the short-term behavior with knowledge about the long-and medium-term behavior improves recommendations. e policy combining short-and long-term interests yields higher accuracy than combining short-and medium-term interests. Contrarily, combining short-and medium-term interests yields higher variety.

Further, the formalization of the proposed approach is presented. e experiments are described in Section 3 and discussed in Section 4, followed be the related work in Section 5. Finally, conclusions are drawn and directions for future works are indicated.

DEFINING READING BEHAVIOR IN NEWS

We consider an online environment in which a publisher provides a collection of news articles to interested readers. Let U = {u m } M m=1 represent the increasing set of visitors. Further, let I = {i n } N n=1 represent the increasing collection of news articles. e publisher observes how visitors act on the website. In particular, the publisher keeps track of events whenever a visitor reads an article. Let θ = (θ u , θ i , θ t , θ c ) represent such an event where the individual variables refer to visitor, article, time, and context. e publisher observers a sequence of events

Θ = {θ (α ) } A α =1 such that θ (α ) t < θ (α +1) t
, ∀α. We represent the reading behavior of individual users as Θ u = {θ : θ u = u}. We assume that visitors have limited time and desire to read articles. In particular, the number of news articles a visitor is willing to read is X N . e publisher tries to engage visitors by providing a small set of suggestions, every time an article is read. Formally, the publisher employs a policy π which takes a given event θ (α ) and automatically produces a ranked list of suggestions S (α ) = {s k |s k ∈ I } K k =1 . e publisher monitors how visitors react upon the received suggestions in order to drive policy improvement. e policy that produced the suggestions gets credited with the reward R whenever a visitor reads any of the suggested articles:

R(π , θ (α ) , S (α ) ) = 1 if ∃β > α, θ (β ) ∈ (Θ u ∩ S (α ) ) 0 otherwise (1)
For practical purposes, the publisher disregards future events if the visitor is inactive for more than a speci ed time τ u . Publishers can employ a variety of policies π ∈ Π. eir objective is to nd the policy that maximizes the cumulative rewards:

π * = arg max π ∈Π θ ∈Θ R(π , θ, S). (2)
We investigate three policies: (a) the short-term news reading; (b) the long-and short-term news reading; (c) the medium-and short-term news reading. ese are further described. Editors put the most recent and signi cant news at the start of newspapers. Analogously, our rst policy suggests articles which have been popular recently. We refer to this policy as baseline and it corresponds to the short-term news reading interests. e baseline has associated a list L of xed size ϵ. As the system observes another event, θ i is added to L. If adding θ i exceeds ϵ, the oldest element is dismissed.

ereby, L continuously stores the most recently read articles. At the same time, the more popular an article is, the more o en it will appear in L.

e baseline policy suggests elements from L, most recently added and di erent from the article being currently read by the user u. Also, the recommendation has an implicit popularity bias as the probability of choosing a list of articles S (α ) depends on f i |L , i ∈ S α , the frequency of article i in L.

e remaining two policies enrich the baseline with information about the news reading process-how readers transition between news categories. A stochastic process models a random system changing over time. Formally, if D is a subinterval of [0, ∞), a continuous-time stochastic process is a set of random variables {X d }, d ∈ D. If we restrict D = N 0 , we obtain a discrete-time stochastic process. Let Ξ = {ξ } V =1 be the nite set of news categories, corresponding to the random variables of the news reading process. Let ξ (θ i ) refer to the category assigned to the article i. Hence, we can transform the reading behavior of an individual user u, Θ u = (θ (1) , θ (2) , . . . , θ (α ) ) into a sequence of categories:

Ξ u = (ξ (θ (1) i ), ξ (θ (2) i ), . . . , ξ (θ (α ) i )
). e Markov property characterizes a stochastic process whose current state captures all information necessary to compute the probability of the next state [START_REF] Kemény | Finite Markov Chains[END_REF]. For the news reading behavior, this is formally de ned as:

Pr[X d +1 = ξ |X d = ξ w , X d -1 = ξ w -1 , . . . , X 0 = ξ w 0 ] = Pr[X d +1 = ξ |X d = ξ w ] = p i j , ∀d ∈ N 0 , ξ w 0 , . . . , ξ w , ξ ∈ Ξ (3)
A transition matrix over all permutations of two categories (|Ξ| = V ) represents the dynamics of such as system:

T =          p 11 . . . p 1V p 21 . . . p 2V . . . . . . . . . p V 1 . . . p V V          p w ≥ 0, ξ w ∈Ξ w p w = 1, ∀ , ξ ∈ Ξ (4)
e transition probabilities p w can be estimated from observations as the ratio between the frequency of transitions from category ξ to category ξ w and the total number of transitions from ξ . If the transition matrix T stays constant as the system evolves, the stochastic process is referred to as First-Order Markov Process.

We use the de ned reading behavior process to create a new recommendation policy on top of the baseline. For each category ξ , there is a separate list L ξ used to model short-term reading interests. For each event θ , we determine the category ξ (θ i ) = ξ . Subsequently, we select the most likely category for the next article: ξ * = arg max p w T and return suggestions from L ξ * . We distinguish the medium-term news reading interests from the longterm ones based on the observations used for estimating T . e matrix corresponding to the long-term behavior contains all the existing observations. e matrix corresponding to the mediumterm behavior contains observations from a limited period only.

EXPERIMENTS

e research questions we address in this paper are:

Histogram of Events per Session

Figure 1: Histogram of events per session (log scale)

Q 1
How stable in time is the news reading behavior, modeled as Markov processes over news categories? Q 2 How do di erent strategies of combining long-, mediumand short-term news reading interests in a recommender compare in terms of performance and news variety?

Data Description

e data set is provided by a German news publisher that accommodates on average 3 million visits per week. It consists of ≈ 196.6 million events, which span 475 days from August 2014 to December 2015. Data from June 16 to June 27, 2015 is missing due to technical problems with the logging system. An event is generated and logged when an article is accessed on the news publisher's website. e events are logged with the following information: the user session's identi er, the event's time-stamp, and the article's link and meta-data, which includes the associated categories. e categories are manually assigned by editors, each time an article is uploaded on the website, and are a set of pre-established keywords (see Table 1). We rely on editors' experiences to assign categories in a consistent manner. Most of the user sessions have less than 10 events as shown in Figure 1.

Data Transformation

Before the experiments, the data has been pre-processed. First, we eliminated events associated to the default session.

is session captures all events with cookies disabled such that individual users cannot be tracked. Second, we chronologically sorted the events within sessions. ird, we segmented sessions into reading episodes. erein, we cut session if events occurred in less than 60 s or longer than 3600 s apart.

ese thresholds were set a er observing and questioning our personal circle about the time spent on article reading. Other sources also suggest an average of 20 min per article 1 .

e transition matrices over news categories have been computed as follows. Consecutive events in a reading episode become a transition, the rst being the source, the second the target. Multiple 1 h p://contentmarketinginstitute.com/2016/01/visitors-read-article/ transitions emerge if either source or target event have several categories assigned. In this case, the Cartesian product of the source and target category sets is calculated, each obtained element becoming a transition.

News Reading Dynamics

e rst research question aims to assess the dynamics of the news reading behavior modeled as a Markov process over news category. We have analyzed the stationarity of the Markov process using a method based on the χ 2 statistical test [START_REF] Bickenbach | Evaluating the Markov Property in Studies of Economic Convergence[END_REF].

e method starts with computing the transition matrix for the complete period τ and the transition matrices for all consecutive sub-intervals t ∈ τ .

en, these local transition matrices are tested against the overall transition matrix for statistical di erence. We formulate the null hypothesis and the alternative hypothesis:

H 0 : ∀t ∈ τ : p w |t = p w ↔ H a : ∃t ∈ τ : p w |t p w
• p w denotes the estimated transition probability from state ξ to state ξ w for the entire period τ ; • p w |t denotes the estimated transition probability from state ξ to state ξ w only for the sub-periods t ∈ τ ;

Given that there are at least two positive values in each row of the overall transition matrix T , the χ 2 test is computed as follows:

Q = t ∈τ ξ ∈Ξ ξ w ∈Ξ z |t (p w |t -p w ) 2 p w ≈ asyχ 2 ξ ∈Ξ (a -1)(b -1) (5)
• z |t denotes the number of observed transitions from state ξ in sub-period t, it could be 0; • Ξ = {w : p w > 0, ξ w ∈ Ξ}, ξ ∈ Ξ; contains all target states observed from state ξ for the entire period τ ; • Q has an asymptotic chi-squared distribution (asyχ 2 ) with the number of degrees of freedom computed as sum over all states ξ ∈ Ξ by considering two terms: a is the number of sub-periods t for which transitions from state ξ are observed; b = |Ξ | is the number of positive values in the row of the transition matrix for the entire period τ .

e test could be also adjusted to assess how much a certain subperiod t di ers from the complete period τ . In this case, the outer sum in (Eq. 5) has 2 terms (|τ | = 2): the period t and the transition matrix computed for all the other periods from τ except t.

e rst prerequisite to conduct the stationarity analysis on our data set is to decide the magnitude of the sub-periods t: days, weeks or months. Periods spanning days or weeks were excluded. In a descriptive analysis, we observed that breaking news changes the local reading pa ern. Also, we randomly selected consecutive days and weeks, and ran the stationarity test. e results showed a signi cant matrix variance (p-value p < 0.001). Consistent with this, Bickenbach and Bode [START_REF] Bickenbach | Evaluating the Markov Property in Studies of Economic Convergence[END_REF] claim that while more granular subsamples are preferred, they should not be too small. Otherwise, nonstationarity could emerge from the test, even though the process is Markov [START_REF] Bickenbach | Evaluating the Markov Property in Studies of Economic Convergence[END_REF]. Eventually, the sub-periods t were set to 1 month.

When working with matrices of millions of events, a di erence caused by few thousands transitions is not necessarily signi cant. However, as the Chi-Square test is very sensitive to the number of observations, it could yield non-stationarity [START_REF] Frankfort-Nachmias | Social Statistics for a Diverse Society[END_REF]. For this reason, we decided to correct the transition frequencies by a factor of 0.001. e values were brought from big-data (millions) to a magnitude equivalent to what has been observed in similar studies (thousands) [START_REF] Bickenbach | Evaluating the Markov Property in Studies of Economic Convergence[END_REF]. A er correction, the frequencies still reached tens of thousands, which we consider a representative sample size.

Recommendation Policy Comparison

e second research question aims to compare the three policies introduced in Section 2 in terms of recommendation performance and ensured news variety. Publishers focus on maintaining the engagement of their readers. us, one way to measure performance is through the cumulative rewards R(π , Θ). Frequently, they normalize the rewards by the number of requests to obtain the click-through rate (CTR) = 1 |Θ | θ ∈Θ R(π , θ, S). More sophisticated evaluation metrics such as the ranking-based ones-normalized discounted cumulative gain or mean reciprocal rank-exist but they cannot be applied in conditions with insu cient user feedback.

Besides monitoring how well readers' preferences are met, we look at the overall variety of suggestions. Having a more diverse set of suggestions can lead to readers experiencing serendipity. Let S (A) refer to all recommendations produced by applying the policy π to the upcoming events Θ. Further, we de ne the number of times an article i is recommended: ν i = s ∈S (A) I(s = i), where I refers to the indicator function which returns 1 if the condition applies and 0 otherwise. Finally, let I (K ) refer to the K items with highest ν i . We quantify overall news variety as,

δ = 1 -i ∈I (K ) ν i |S (A) | . (6)
In other words, the number of recommendations subsumed by elements of I (K ) is normalized by the total number of recommendations and subtracted from 1. us, δ ∈ [0, 1] with δ = 0 referring to the case that all readers received the identical K suggestions and δ = 1 signaling that all readers received disjoint sets of suggestions. We use the same data for this evaluation as has been used for the stationarity analysis in the previous section. is allows us to have a sound and transparent base for comparing the recommender outcomes, especially for changing periods or strongly deviating months. e evaluation follows a sliding-window approach. First, the three news recommendation algorithms are initialized with 10 000 events from September 2014 and the initial transition matrix is computed for August 2014. Subsequently, all events are processed in chronological order. Each recommendation algorithm computes a list of K = 4 suggestions. e choice of 4 suggestions corresponds to the number of suggestions displayed on the website of the news publisher. en, for each user associated with the suggestions, it is checked whether they accessed one of the recommended articles within an hour. Having exceeded the 1 h limit, the suggestions are discarded. en, the cumulative rewards and news variety are hourly computed. is yields a total of 11 355 measurements.

RESULTS AND DISCUSSION

is section presents the results of our experimentation. Section 4.1 is devoted to the stationarity analysis. Section 4.2 shows the observation regarding the recommendation policies.

News Reading Dynamics

We assessed the stationarity of the news reading behavior, modeled as a Markov process over news categories, for the entire period of August, 2014-December, 2015. Using the Chi-Square test (Equation 5), we obtained Q = 8709.744 with df = 1778 degrees of freedom, leading to the rejection of H 0 (p < 0.01). us, the transition probabilities uctuate during the 17-month period. Further, we have checked whether longer periods exhibit stationary patterns. For ge ing be er insights into how to nd these periods, and whether they existed, we used the Chi-Square test (Equation 5) adjusted for computing individual period di erence: |τ | = 2; the rst sum term is t, the assessed month; the second sum term is the rest of the period τ except this month. en, the resulting Q values per month were plo ed. ereby, we observed a major structural break from March 2015 to April 2015. en, the test was re-run for the period August 2014 and March 2015 still rejecting H 0 with Q = 1124.85, df = 938, p < 0.01. However, when Q values for each month of this period were plo ed, a potential strong similarity has been observed between September, 2014 and February, 2015. Indeed, the Chi-Square test con rmed this similarity, accepting the null hypothesis with Q = 700.51, df = 645, p > 0.05. We repeated this procedure for the period April, 2015-December, 2015. Finally, the following homogeneous periods have been discovered: e y-axis represents the transition probabilities greater than 0.05 from category 7-General News, to all news categories; the x-axis re ects the month. e discovered homogeneous periods become visible: the circles for the months within this period line up almost horizontally. Further, we noticed a tendency to read within the same category.

e highest transition probability corresponds to category 7, same as the source. Finally, it appears that few target categories are preferred to be read next, a er the source category, over the entire period: Science, Communication-code 2; Politics, Business Economics-code 4; Travel, Tourism, Navigation-code 5. However, their priority changes in time. For instance, travel and tourism news are read most frequently together with the general news in the beginning of the year (January-March). We created visualizations for other source categories as well observing similar results ( gures omi ed for space reason). O en, in solutions, the model stationarity is taken for granted. However, these results show that news reading behavior has both stable and dynamic parts.

e most preferred news categories read a er a given category seem to be stable. By contrary, the transitions between these news categories are stationary for limited periods of time, with some singular months with strongly deviating pa erns (e.g. August, 2014 and 2015). Consequently, the preceding month appears to be o en a suitable base for recommendation, except for the months with strongly deviating reading pa erns or starting a homogeneous period. Being able to identify the cause of these changes and predict them could be very useful for adjusting dynamically the recommendation. We contemplate that various factors could in uence news reading behavior: changes in human habits induced by seasons, structural changes of news publishers' websites or very important news events spanning longer periods.

Recommendation Policy Comparison

Figure 3 presents the performance results of the proposed strategies for recommendation: short-term interests only (baseline (B)), short-and long-term interests (transition complete (T c )), short-and medium-term interests (transition 1 month (T 1 )). On the le -hand side, each curve re ects the proportion of per-hour measurements, y-value, exhibiting a maximum response rate of x-value. For instance, baseline intersects the ordinate at ≈ 0.27, meaning that about 27 % of per-hour measurements have associated a 0.0 % response rate. Alternatively, T c achieves in about 80 % of the cases a response rate up to 10 %. us, a more distant curve from the top le corner is preferred. e right-hand side of Figure 3 shows the pair-wise comparison of the recommendation strategies: transition 1 month vs. baseline, transition complete vs. baseline, and transition 1 month vs. transition complete. e x-axis conveys the per-hour measurements in chronological order, the le -most point being the rst hour of September 1, 2014, the right-most point the last hour of December 31, 2015. e y-axis values illustrate the di erence between the response rates of each pair of measurements. Positive values are shown in blue and negative ones in red.

Both T 1 and T c achieve higher response rates on average than the baseline. T c performs similarly to or even be er than T 1 . Analyzing the granular measurements, we observe that T 1 falls short of T c in particular during the months: March, May, November and December, 2015. e subpar performance of T 1 could be explained for March-strongly deviating month from February 2015, its base of prediction, and November-the rst month of a new homogeneous period so di erent than October 2015. Nonetheless, May and December, 2015 have associated low response rates even though they were very similar to their preceding months. is outcome could be explained by what happens in the second part of the recommendation, when the short-term interests strategy is used. e dynamic data structures of articles are sensitive to the latest crowd reading behavior. It appears that there is a division among the crowd reading interests, hypothetically caused by the co-existence of multiple strongly in uential news, competing in popularity. Another interesting aspect is that sometimes strongly deviating months or the last months of homogeneous periods are a good base for prediction such as March, July and August, 2015.

is indicates that even if the overall reading behavior changes, the most likely transition from a given source category stays consistent. erefore, even for time-variant periods, the target category associated to a source appears stable.

Further, Figure 4 plots the histogram with news variety measurements for T 1 and T c , hourly computed with Equation 6 and K set to 4. We observe that T 1 varies suggestions be er than T c as shown by the right-most peak in Figure 4. e long-term interests represent the transition probabilities in the long-run between news categories. Aligned with the literature [START_REF] Das | Google News Personalization: Scalable Online[END_REF][START_REF] Orson | Changing Pa erns of News Consumption and Participation[END_REF], this reading behavior converges to few most preferred categories chosen in recommendations. However, such behavior is not sensitive to local strong trends such as season-induced changes that could temporally modify the most likely transitions. Contrarily, the medium-term interests strategy is able to overcome this, leading to a higher news variety.

Re ections on News Recommendation

e proposed solution shows that categories provide noticeable improvements despite their simplistic nature and a fair level of personalization when rich user data is missing. Categorical information is readily available, thus it could be implemented as-is in other news recommendations setups.

Its simplicity ensures scalability and maintainability; the crowdbased models for recommendations and the lack of authentication ensures high privacy. Data sparsity is not an issue as the proposed hybrid solution uses categories, which are limited and mostly stable in time, and the dynamic data structures are pruned to store a xed number of articles. e proposed analysis of news reading dynamics could support decisions about which data to use for building models. Our experiments show that by varying the time horizon publishers can trade some accuracy for news variety. Accuracy re ects how well the news recommender anticipates readers' preferences. Variety indicates to what degree readers are exposed to di erent articles. Which should publishers prioritize? Mark ompson (CEO of the New York Times) [START_REF] Mark Ompson | Challenging New Economics of Journalism[END_REF] lists major challenges that news publishers face as they compete online. Revenue from printed newspapers declines. Publishers have to counteract the downward trend by a racting users online. Hereby, they cannot negotiate as comfortably as they used to for the printed news. Digital social media sites constitute the major players in digital advertising. us, successfully harnessing users' a ention is crucial to generate online revenues. is suggests focusing on accuracy. Give readers what they want to read to keep their sessions alive. At the same time, news consumption a ects society, economy, and politics. Rasmus Kleis Nielsen (Director of Research, Reuters) [START_REF] Rasmus Kleis Nielsen | People Want Personalised Recommendations (Even as they Worry about the Consequences)[END_REF] nds that worldwide people prefer personalized news recommendations. Still, he points out that this may create " lter bubbles". ese are spaces in which people consume similar news, reinforcing their existing believes and opinions. ey represent a serious danger to social, economic, and political discourse as they prevent exposure to deviating opinions. is favors news variety over accuracy. Finding the right balance between both aspects remains a major challenge for publishers to address in the future. Maximizing pro ts may lead to social divides. Optimizing news variety may yield economic damage to the publisher. Publishers may have to nd additional sources of revenue to compensate reduced accuracy. Readers may have to spend money to have access to high-quality journalism.

LITERATURE REVIEW

Traditionally, recommender solutions are of grouped into two types [START_REF] Adomavicius | Toward the Next Generation of Recommender Systems: A Survey of the State-of-the-Art and Possible Extensions[END_REF][START_REF] Garcin | Personalized News Recommendation Based on Collaborative Filtering[END_REF][START_REF] Javari | A Probabilistic Model to Resolve Diversity-Accuracy Challenge of Recommendation Systems[END_REF]. A content-based recommender suggests items similar to previously liked ones. A collaborative recommender suggests items by comparing user preferences. Hybrid solutions are frequently reported to perform best in the news domain [START_REF] Ahmed | Fair and Balanced : Learning to Present News Stories[END_REF][START_REF] Billsus | Adaptive News Access[END_REF][START_REF] Das | Google News Personalization: Scalable Online[END_REF][START_REF] Jonnalagedda | Incorporating Popularity in a Personalized News Recommender System[END_REF][START_REF] Li | Modeling and Broadening Temporal User Interest in Personalized News Recommendation[END_REF][START_REF] Liang | Discovering User Interests from Web Browsing Behavior: An Application to Internet News Services[END_REF][START_REF] Liu | Personalized News Recommendation Based on Click Behavior[END_REF].

News Reading Interests

News readers' interests are seldom expressed explicitly. Common approaches discover readers' interests from click behavior and articles' categories [START_REF] Li | A Contextual-Bandit Approach to Personalized News Article Recommendation[END_REF]. In some cases, the categories of the articles are already de ned in advance and represented as contextual meta-data [START_REF] Das | Google News Personalization: Scalable Online[END_REF][START_REF] Doychev | An Analysis of Recommender Algorithms for Online News[END_REF][START_REF] Epure | Process Mining for Recommender Strategies Support in News Media[END_REF]. In other cases, categories are discovered automatically and represented either as more granular vocabularies associated to the categories [START_REF] Ahmed | Fair and Balanced : Learning to Present News Stories[END_REF][START_REF] Billsus | Adaptive News Access[END_REF][START_REF] Liang | Discovering User Interests from Web Browsing Behavior: An Application to Internet News Services[END_REF], or keywords de ning general, well-known topics such as sport or politics [START_REF] Li | Modeling and Broadening Temporal User Interest in Personalized News Recommendation[END_REF][START_REF] Liu | Personalized News Recommendation Based on Click Behavior[END_REF][START_REF] Wu | Personalized News Filtering and Summarization on the Web[END_REF]. In most of these works, once known, recommenders use the categories as explicit knowledge. Other approaches do not infer users' interests but incorporate them implicitly as built-in features. For instance, Billsus et al. [START_REF] Billsus | Adaptive News Access[END_REF] and Li et al. [START_REF] Li | Modeling and Broadening Temporal User Interest in Personalized News Recommendation[END_REF] address short-term interests through standard content-based components by recommending articles similar to what users recently read.

Multiple authors distinguish short-term and long-term interests [START_REF] Billsus | Adaptive News Access[END_REF][START_REF] Li | Modeling and Broadening Temporal User Interest in Personalized News Recommendation[END_REF][START_REF] Liu | Personalized News Recommendation Based on Click Behavior[END_REF]. e long-term interests are considered by most of related works as the users' genuine interests which are less likely to change over time. Contrarily, the short-term interests are discovered from the most recent reading behavior and could represent deviations from the long-term interests triggered by momentary events such as breaking news or interesting reading discoveries. Billsus et al. [START_REF] Billsus | Adaptive News Access[END_REF] prioritize the short-term interests model in the formulation of recommendations in Google News as they claim it is more sensitive to changes. In a follow-up work on the Google News system, Liu et al. [START_REF] Liu | Personalized News Recommendation Based on Click Behavior[END_REF] showed that the short-term interests of an individual user follow closely the short-term interests of the general public. Consequently, in this updated solution, they use the unpersonalized model to address the short-term interests, and the individual user's past reading behavior for predicting categories within the long-term interests. Another mechanism for manipulating the changes in users' preferences is decaying older interests in favor of the newest ones [START_REF] Das | Google News Personalization: Scalable Online[END_REF][START_REF] Li | Modeling and Broadening Temporal User Interest in Personalized News Recommendation[END_REF].

We distinguish between short-and long-term news reading interests as well, but we implement them di erently. e short-term interests are implicitly captured by providing recommendations according to the real-time popularity distribution of the news, most recently read by the crowd. e long-term behavior is explicitly captured with transitions over news categories, dynamically maintained. We also introduce a new type of behavior corresponding to the medium-term interests. Additionally, our models are created per-crowd and from data re ecting engaged reading.

News Recency, Popularity, and Variety

While some news articles could be relevant even weeks or months a er their publication, others get quickly outdated for the majority of users. us, recency and popularity are o en considered in news recommendation [START_REF] Ahmed | Fair and Balanced : Learning to Present News Stories[END_REF][START_REF] Billsus | Adaptive News Access[END_REF][START_REF] Das | Google News Personalization: Scalable Online[END_REF][START_REF] Doychev | An Analysis of Recommender Algorithms for Online News[END_REF][START_REF] Jonnalagedda | Incorporating Popularity in a Personalized News Recommender System[END_REF][START_REF] Liang | Discovering User Interests from Web Browsing Behavior: An Application to Internet News Services[END_REF].

Billsus et al. [START_REF] Billsus | Adaptive News Access[END_REF] report that their recommender takes a list of articles as input, which have been selected in advance by several criteria including recency. Likewise, in the work of Li et al. [START_REF] Li | Modeling and Broadening Temporal User Interest in Personalized News Recommendation[END_REF] a probabilistic graphical model is built with recent articles. Furthermore, Das et al. [START_REF] Das | Google News Personalization: Scalable Online[END_REF] choose to re-build the recommender models every hour in order to present the freshest information to the users. ey use a covisitation metric in recommendation that captures the relative popularity (what is the most popular article visited together with the current read article). Similarly, in Yahoo's news aggregator, articles are selected to represent both new and popular events [START_REF] Ahmed | Fair and Balanced : Learning to Present News Stories[END_REF]. For this, they consider articles' timestamps and key events identi ed through the events' count distribution within the selected article set [START_REF] Ahmed | Fair and Balanced : Learning to Present News Stories[END_REF]. Liang et al. [START_REF] Liang | Discovering User Interests from Web Browsing Behavior: An Application to Internet News Services[END_REF] set a higher weight to the most recent articles from those discovered by using models for short and long-term interests. External knowledge from Twi er can also be used to determine popularity [START_REF] De | From Cha er to Headlines: Harnessing the Real-time Web for Personalized News Recommendation[END_REF][START_REF] Jonnalagedda | Incorporating Popularity in a Personalized News Recommender System[END_REF][START_REF] Phelan | Using Twi er to Recommend Real-time Topical News[END_REF].

While popularity and recency are common news features included in recommender systems, variety is mentioned as a future work in some papers [START_REF] Billsus | Adaptive News Access[END_REF][START_REF] Das | Google News Personalization: Scalable Online[END_REF] or less relevant in some others [START_REF] Ahmed | Fair and Balanced : Learning to Present News Stories[END_REF][START_REF] Doychev | An Analysis of Recommender Algorithms for Online News[END_REF]. e users' long-term interests model used in [START_REF] Billsus | Adaptive News Access[END_REF] does not vary the recommendations for users with similar pro les but per user. Das et al. [START_REF] Das | Google News Personalization: Scalable Online[END_REF] limit the news variety to the cluster of similar users. Li et al. [START_REF] Li | Modeling and Broadening Temporal User Interest in Personalized News Recommendation[END_REF] handle news variety explicitly through random walks in the user-item a nity graph created in advance. Likewise, a probabilistic model is proposed in [START_REF] Javari | A Probabilistic Model to Resolve Diversity-Accuracy Challenge of Recommendation Systems[END_REF] to address this aspect. In contrast, having analyzed the transition matrices extracted from the CLEF NewsREEL 2014 competition data set, Doychev et al. [START_REF] Doychev | An Analysis of Recommender Algorithms for Online News[END_REF] show that users tend to read news from the same category. Moreover, few dominant categories and clicks between these categories account for the majority of items that are read [START_REF] Doychev | An Analysis of Recommender Algorithms for Online News[END_REF]. Driven by similar reasons, Ahmed et al. [START_REF] Ahmed | Fair and Balanced : Learning to Present News Stories[END_REF] provide recommendations within the same category-story, but they also focus to some extent on other tangential categories as they claim users are interested in di erent aspects of an event (e.g. political, economic).

In the short-term reading policy, we consider popularity and recency, without any external sources. As for news variety, we do not prioritize it for categories, but we ensure that two readers are likely to be recommended di erent articles in a short time window.

Session-based Recommendation

Session-based recommender systems focus on sessions rather than on complete user pro les. Shani et al. [START_REF] Shani | An MDP-Based Recommender System[END_REF] highlight the sessionbased character of recommender systems deployed in business setups. ey move the underlying model from a matrix completion task toward a Markov Decision Process (MDP). Businesses encounter session-based recommender systems in domains including music [START_REF] Yang | Local Implicit Feedback Mining for Music Recommendation[END_REF], products [START_REF] Hong | Product Recommendation with Temporal Dynamics[END_REF], and news [START_REF] Li | A Contextual-Bandit Approach to Personalized News Article Recommendation[END_REF]. Deep learning architectures have been applied to session-based recommendation (cf. Hidasi et al. [START_REF] Hidasi | Session-based Recommendations with Recurrent Neural Networks[END_REF], and Tan et al. [START_REF] Kiam Tan | Improved Recurrent Neural Networks for Session-based Recommendations[END_REF]), achieving promising results. Still, they involve a multitude of parameters to optimize. Our method circumvents the e orts to tune as many parameters albeit sacri cing accuracy to some degree.

In our solution, sessions are analyzed and split in reading episodes in case of too short or long reading times between consecutive events. Related works that approached the session spli ing to some degree are [START_REF] Liang | Discovering User Interests from Web Browsing Behavior: An Application to Internet News Services[END_REF][START_REF] Yang | Personalizing Web Page Recommendation via Collaborative Filtering and Topic-Aware Markov Model[END_REF]. Nonetheless, the former considers fast browsing only, se ing a time within 3 s to 250 s, while the later splits in sub-sessions of 30 min, without reasoning on clicks.

Process Models for Recommendation

Several works model news recommender solutions as probabilistic graphical problems [START_REF] Ahmed | Fair and Balanced : Learning to Present News Stories[END_REF][START_REF] Li | Modeling and Broadening Temporal User Interest in Personalized News Recommendation[END_REF][START_REF] Sahoo | A Hidden Markov Model for Collaborative Filtering[END_REF]. Li et al. [START_REF] Li | Modeling and Broadening Temporal User Interest in Personalized News Recommendation[END_REF] consider the states in the process being both users and articles and the possible transitions are user to article, article to article and article to user. ey populate the transition matrix with similarity scores between articles and between users and articles [START_REF] Li | Modeling and Broadening Temporal User Interest in Personalized News Recommendation[END_REF]. Ahmed et al. [START_REF] Ahmed | Fair and Balanced : Learning to Present News Stories[END_REF] use a transition graph with three types of states: views, clicks, and documents where the views are considered latent variables. e probability of a click to happen is conditioned on the current document, current view, and the previous click [START_REF] Ahmed | Fair and Balanced : Learning to Present News Stories[END_REF]. Sahoo et al. [START_REF] Sahoo | A Hidden Markov Model for Collaborative Filtering[END_REF] choose a Hidden Markov Model where users are the observed variables and the latent classes represent globally preferred consumption pa erns per month. Yang et al. [START_REF] Yang | Personalizing Web Page Recommendation via Collaborative Filtering and Topic-Aware Markov Model[END_REF] propose a topic-aware Markov model for recommending web pages. Segments of sessions-consecutive web page visits, are transformed to temporal states while sequences of articles of the same topic become topical states. e prediction of a page to a user considers the similarity to other users and the probability of observing the user's session containing the predicted page [START_REF] Yang | Personalizing Web Page Recommendation via Collaborative Filtering and Topic-Aware Markov Model[END_REF]. ough e ective, the presented models are di cult to maintain with an increasing number of clicks, users, and items. Categories in our approach are a more stable choice. Also, these solutions rely on much longer sessions. Yang et al. [START_REF] Yang | Personalizing Web Page Recommendation via Collaborative Filtering and Topic-Aware Markov Model[END_REF] report an average of 3700 clicks per user and others in the news domain [START_REF] Li | Modeling and Broadening Temporal User Interest in Personalized News Recommendation[END_REF][START_REF] Liang | Discovering User Interests from Web Browsing Behavior: An Application to Internet News Services[END_REF][START_REF] Liu | Personalized News Recommendation Based on Click Behavior[END_REF] select only authenticated sessions of minimum 10 clicks. As per-user models are likely unsuitable in short sessions, we aggregate knowledge on news consumption over time and introduce personalization in sessions. Compared with the other works, Sahoo et al. [START_REF] Sahoo | A Hidden Markov Model for Collaborative Filtering[END_REF] also uses successfully a global model with personalization based on users' cases, but with reported high computational costs.

News Reading Behavior Analysis

In order to learn more about users' reading behavior and lead the design of the news recommenders, certain works performed highscale user log analysis over time [START_REF] Esiyok | Users' Reading Habits in Online News Portals[END_REF][START_REF] Li | Modeling and Broadening Temporal User Interest in Personalized News Recommendation[END_REF][START_REF] Liu | Personalized News Recommendation Based on Click Behavior[END_REF].

Li et al. [START_REF] Li | Modeling and Broadening Temporal User Interest in Personalized News Recommendation[END_REF] compared the reading behavior regarding long-and short-term interests. ey considered short-term interests being the categories preferred by users within a time window of three days and similarly, the long-term interests were associated with a time period of 15 days. Moreover, three groups of users were created based on the click frequency and separately analyzed. e approach they used for comparison was to plot the Kullback-Leibler (KL) divergence scores, averaged per group, and computed between two consecutive periods for each user. Results showed that long-term goals are quite stable while the short-term ones vary signi cantly. Liu et al. [START_REF] Liu | Personalized News Recommendation Based on Click Behavior[END_REF] conducted their analysis by visually projecting the differences between categories' distribution for each user, for each two consecutive months, and also the click distribution for each category per month. e news categories are general topics such as world news, sport, entertainment, and the distribution is represented by the averaged number of clicks per category. ey discovered that the current preferences of users change, that the general public interests follow the big events trend (e.g. national news are read more during elections) and, to certain extent, that the individual users' interests follow those of the general public. Esiyok et al. [START_REF] Esiyok | Users' Reading Habits in Online News Portals[END_REF] analyzed immediate transitions between categories.

Compared to these works, we assess the dynamics of the news reading behavior through the stationarity of the Markov process across news categories. To our knowledge, we are the rst to investigate the evolution of news categories relations over time.

CONCLUSION AND FUTURE WORKS

With the digital transformation, news has started being created and delivered by many entities besides news publishers. News consumers are exposed nowadays to increased sources of information and setups. In this highly competitive ecosystem, news publishers have sought ways to engage existing and a ract new readers. Hence, personalized news recommendation has become a key element in their strategy. e proposed solutions aimed to ensure the delivery of fresh and interesting news, taking into consideration the dynamics of reading interests.

Still, limited research has tackled news personalization in hybrid recommenders when extensive user pro le are unavailable. Most of the news publishers face this issue as the website authentication is a rare habit among online readers and the cookie-identi cation has its limits. In this paper, we prove that a recommender based on the dynamics of reading behavior, news popularity, and recency can provide a basic level of personalization and comply with the main domain constraints revealed by the related solutions. Speci cally, we design and compare three variants of news recommendation centered on short-, medium-, and long-term reading interests. e short-term interests are captured at the article level, by recommending news recently read by the crowd, biased by popularity. e medium-and long-term interests are captured at the news category level, by predicting the next category to be read from the current user session and the Markov process over all categories. Moreover, the dynamics of the news reading behavior modeled as transitions between news categories is assessed over an extensive period.

e proposed research questions are addressed through experimentation with real data from a German news publisher. First, news readers appear to stay loyal to certain categories, which are frequently read together within a reading episode. Nonetheless, the priority for these relations, captured by transitions, changes in months possibly because of seasons, popular events lasting longer time, or structural changes in publishers' websites. Second, augmenting a short-term interests recommendation policy with the long-or medium-term behavior leads to higher response rates. Considering the transitions between news categories from the previous month leads to a higher variety than the long-term transitions.

Several limits could be identi ed nonetheless. e proposed solution handles cold start well on the user side. Still, it cannot immediately recommend new items until they have been read at least once. Moreover, the dynamic data structures storing articles can have the popularity manipulated arti cially, propagating thus a fake lter bubble. Currently, the recommendation of news could be sometimes redundant as the solution checks only the user's most recent read. Parameters are present in the solution: ϵ the size of article bu ers per category, the thresholds involved in the identi cation of reading episodes. Future work should explore other values of these parameters and their e ect on recommendations. Online evaluation will show how well this approach performs under real-life conditions. Also, evaluation with additional data sets and with di erent session-based recommenders is required.

More content and context features could be considered in the future extending the proposed approach. In the current solution, the news categories are manually associated to the articles by editors or journalists. An alternative approach is to use more of the news content data directly. If humans are biased or inconsistent in their tagging practices, algorithms for category assignment could assist them or augment in the back-end the manually a ached category set. Solutions for classifying news articles into categories or representing the data items in terms of semantic entities with links to conceptual abstractions have already been proposed [START_REF] Ahmed | Fair and Balanced : Learning to Present News Stories[END_REF][START_REF] Capelle | Semantic News Recommendation Using WordNet and Bing Similarities Categories and Subject Descriptors[END_REF][START_REF] Marco De Gemmis | Semantics-Aware Content-Based Recommender Systems[END_REF][START_REF] Liang | Discovering User Interests from Web Browsing Behavior: An Application to Internet News Services[END_REF].

External sources of knowledge such as social media could be explored for identifying the most recent trends and injecting news variety in recommendations by external drivers [START_REF] Gao | Interweaving Trend and User Modeling for Personalized News Recommendation[END_REF][START_REF] Hashavit | E ective Trend Detection within a Dynamic Search Context[END_REF][START_REF] Vallet | Characterizing and Predicting Viral-and-Popular Video Content[END_REF]. Suggestions at the category level in the form of a time-line is another option to be explored [START_REF] Tran | Timeline summarization from relevant headlines[END_REF]. For this, the variety of the recommended categories should be ensured. e literature revealed that there is a lack of agreement about how short-and long-term interests are de ned. For instance, long-term is limited to 15 days in [START_REF] Li | Modeling and Broadening Temporal User Interest in Personalized News Recommendation[END_REF] and to a month in [START_REF] Liu | Personalized News Recommendation Based on Click Behavior[END_REF]. However, in order to more easily integrate conclusions emerging from various studies, a sound, well-documented framework should be proposed. Finally, our approach could be applicable to other domains where the item consumption is sequential within the same session, and items have a relevant feature associated or inferred.

is could be the case for music or job recommendation.

•

  Sep. 2014-Feb. 2015: Q = 700.5, df = 645, p > 0.05, • Apr. 2015-Jul. 2015: Q = 6.56, df = 48, p > 0.99, • Sep. 2015-Oct. 2015: Q = 14.95, df = 55, p > 0.99, • Nov. 2015-Dec. 2015: Q = 62.16, df = 47, p > 0.05.

Figure 2

 2 Figure 2 is consistent with these ndings.e y-axis represents the transition probabilities greater than 0.05 from category 7-General News, to all news categories; the x-axis re ects the month. e discovered homogeneous periods become visible: the circles for the months within this period line up almost horizontally. Further, we noticed a tendency to read within the same category.e highest transition probability corresponds to category 7, same as the source. Finally, it appears that few target categories are preferred to be read next, a er the source category, over the entire period: Science, Communication-code 2; Politics, Business

7 Figure 2 :

 72 Figure 2: Probabilities per month for transitions from General News-code 7, to all categories in 2015.

Figure 3 :

 3 Figure 3: e le -hand side shows the cumulative distribution function (CDF) of the hourly response rates for the compared recommenders.e right-hand side shows the pair-wise comparison. Long and medium-term interests improve the results over short-term interests only. e strategy based on the long and short-term interests yields consistently the best results.

Figure 4 :

 4 Figure 4: Analysis of the news variety ensured by T 1 and T c . e two histograms show the distribution across the spectrum. T 1 has a peak of very high variety, which T c lacks.

Table 1 :

 1 News categories and associated codes

	Cars, Motor, Tra c	1 Science, Communication	2 Games, Virtual World, Toys	3
	Politics, Business, Economics	4 Travel, Tourism, Navigation	5 TV, Radio, Video, Photo	6
	General News	7 Professional, Career	8 Computers, Technology	9
	Family, Education, Leisure	10 Banking, Finance, Insurance 11 Health, Sports, Nutrition	12
	Real Estate, Home, Gardening 13 People, Relationships	14 Fashion, Lifestyle, Culture	15