Bo Xie

Xianjia Wang

Chuan Zhou

Competitive and Cooperative Degree in Supply Chain: Supplier Selection between Competitors and Third-party Suppliers

Keywords: Outsourcing selection, Co-opetition, Price competition

This work explores the firm's supplier selection question that the competitor firm and the third-party supplier can supply the substitutable component. We consider a supply chain with two competing original equipment manufacturers (OEMs) and two third-party suppliers. The two OEMs produce the competing products which are comprised by two main components. Each OEM only can produce one component in-house and each third-party supplier only supplies one component. The OEMs must decide whether to outsource the other component to the competing OEM or to a third-party supplier. We discuss the different supplier selection strategies and compare the OEMs profits in different decision. We find that there are conditions where, while one of the OEMs should outsource to the competing firm, outsourcing to a third-party supplier is the optimal strategy for the other OEM even when the cost of the third-party supplier is more expensive and the competition is intense.

Introduction

Competitors Nissan and Daimler's Mercedes are both outsourcees and outsourcers of one another. In particular, Nissan uses Daimler's Mercedes front-wheel-drive architecture platform for its Infiniti luxury vehicle and supplies Daimler with diesel and gas engines [START_REF] Chappell | Daimler planners confer weekly on more deals[END_REF]. Moreover, Samsung and Google, Leica and Panasonic also compete with the end product in the market and supply the component to each other in the component wholesale market [START_REF]Panasonic introduces ultra-slim LUMIX digital cameras with 10x optical zoom and wide-angle Leica lens[END_REF] [START_REF]Leica goes digital. Digital Life[END_REF]. We call this relationship is co-opetition. The previous research literature (e.g., [START_REF] Venkatesh | Research note-sole entrant, co-optor, or component supplier: optimal end-product strategies for manufactures of proprietary component brands[END_REF] [5] [START_REF] Wang | On the advantage of quantity leadership when outsourcing production to a competitive contract manufacturer[END_REF]) has researched the co-opetition problem about the competing firms and points out that the competitor can earn revenue from both competing products with two firms cooperation. Consequently, the competitor will not price its own product aggressively, thus leading to a lower degree of competition.

In this paper, we consider the supplier-selection problem of two competing OEMs. The OEMs can produce one component in-house and must outsource the other component to a third-party supplier or to the competing OEM. We consider the case where competing OEMs will cooperate as supply-chain partners only if their total profit is higher than that they can get if they do not cooperate.

Literature Review

Our work in this paper relates to the stream of research on the topic of forming a supply-chain relationship with competitors. Bengtsson and Kock [START_REF] Bengtsson | Coopetition" in business networks-to cooperate and compete[END_REF] define the co-opetition as a strategy embodying simultaneous cooperation and competition between firms. Gnyawali and Park [START_REF] Gnyawali | Co-opetition between giants: Collaboration with competitors for technological innovation[END_REF] investigate why and how co-opetition between large firms and impact the participating firms and the industry. Venkatesh et al. [START_REF] Venkatesh | Research note-sole entrant, co-optor, or component supplier: optimal end-product strategies for manufactures of proprietary component brands[END_REF] find that the manufacturers of proprietary component brands often have the ability to effect for the end-products and face to choose its role from sole entrant, co-optor and component supplier in the market. Lim and Tan [START_REF] Lim | Outsourcing suppliers as downstream competitors: biting the hand that feeds[END_REF] focus on the outsourcing of the supplier as a direct competitor of the buyer firm in the downstream marketplace. Wang et al. [START_REF] Wang | On the advantage of quantity leadership when outsourcing production to a competitive contract manufacturer[END_REF] investigate the advantage of being the first mover when a contract manufacturer acts as both supplier and competitor. Moreover, Pun [START_REF] Pun | Supplier selection when the production process can be improved[END_REF] considers the supplier selection of a manufacturer can either outsource to an independent supplier or to its competitor when the firm wants to do R&D for the end-product.

Our paper differs from these papers in the following ways: First, we assume that there are two OEMs competing in the market and each of them can supply the component with its brand. Second, in our paper, we assume that there is price competition for the OEMs in the market. The OEM's decision about the outsourcing will have impact for total profit. To the best of our knowledge, the case that the competing OEMs supply the component to each other is a new type in supply chain.

Model Framework

We consider a supply chain with two OEMs (firms G and S) and two third-party suppliers and each OEM sales a competing product. And each product consists of two main components, A and B. Firm G (firm S) produces component A (component B) in-house and must outsource component B (component A) either to a third-party supplier firm B (firm A) or to the competing OEM firm S (firm G). The basic frame work of the model is similar to that in Pun and Bo [START_REF] Pun | The more the better? Optimal degree of supply-chain cooperation between competitors[END_REF]. The OEM and the third-party supplier each produce the component with different costs. Specifically, the production cost of the OEMs is normalized to zero, and the production cost of the third-party suppliers is c  , so that the third-party suppliers have higher production cost when 0  c and the OEMs have higher production cost when 0  c . We use the demand model presented by McGuire and Staelin [START_REF] Mcguire | An industry equilibrium analysis of downstream vertical integration[END_REF] and Staelin [START_REF] Staelin | Commentary -an industry equilibrium analysis of downstream vertical integration: Twenty-five years later[END_REF]. Specifically, the demand of the end-product of firm } , { S G i  is as follows:

i i i p p D         1 1 1 1 (1)
i is the competitor of firm i and pi is the price of product i .

1 0   
is the degree of competition between the two products, so that the two products are not competing when is zero and competition increases in .

In this game, the two OEMs first choose the outsourcing strategy. Then, as commonly assumed in the literature (e.g. [START_REF] Gerchak | Revenue-sharing vs. wholesale-price contracts in assembly systems with random demand[END_REF] [15] [START_REF] Nagarajan | Coalition stability in assembly models[END_REF]), the suppliers set the wholesale price w simultaneously. Lastly, the two competing OEMs choose the retail price p .The OEM can choose to outsource to the competing OEM or to the third-party supplier. We use the abbreviations T and C (Third-party supplier and Competing OEM) to denote the different suppliers and the first letter to denote the component supplier for firm G and the second letter for firm S. As a result, we will get four strategies as {TT, TC, CT, CC}, where TT denotes that firm G outsources to the third-party supplier and firm S outsources to the third-party supplier. Since strategy TC and strategy CT are symmetrical, without loss of generality, we omit the results of strategy CT to avoid redundancy.

G B G D w p ) (  S G G B G D w D w p   ) ( S G G S G D w D w p   ) ( S (OEM) S A S D w p ) (  S G S D w p ) (  G S S G S D w D w p   ) ( A (3 rd -party) S A D c w ) (   0 0 B (3 rd -party) G B D c w ) (   G B D c w ) (   0
In the next analysis, we will use G  and S  to represent the profits of the two OEMs. And we use superscripts to denote the outsourcing strategy. For example, TC S  represents the profit of firm S under strategy TC. Specifically, firm G and firm S will cooperate (strategy TC or CC) only if they can earn higher total profits than they do under strategy TT. For notational convenience, let the total profit of the two firms be

S G GS     
. Then the two competing OEMs will choose the strategy that gives them the largest total profit.

Analysis

In this section, we will compare total profit and present the optimal outsourcing strategy. Throughout the analysis, we will focus on the region where demands of both products are positive. We consider another strategy that two OEMs both produce the two main components in-house and use II GS  as the total profit under this strategy. We will compare the total profit in the following and discuss the competition and cooperation. Proposition 1. The comparisons of the total profit of the two OEMs are as follows. a.

) ( 0         c TT GS II GS , b. 3 13        CC GS II GS , c. ) ( 1         c CC GS TT GS , d. ) ( 2         c TC GS TT GS , e. ) ( ) ( 3 3     b a CC GS TC GS c        .
It has been shown that selling through third-party retailers can mitigate competition between competing manufacturers (e.g. [START_REF] Mcguire | An industry equilibrium analysis of downstream vertical integration[END_REF]). Proposition 2a states that competition between OEMs can also be mitigated with outsourcing to the third-party suppliers. In particular, when the two products are independent ( 0   ), the OEMs should produce both components in-house (strategy II) in order to eliminate the inefficiency of double marginalization unless the third-party supplier has a very low cost ( Dc < G 0 (0) = -1 ). It can be shown that G 0 (q ) increases in q . Consequently, when compared to strategy II, strategy TT becomes more valuable as competition intensifies since outsourcing to a third-party supplier can mitigate competition.

Since the third-party suppliers are not responsible for producing any of the components under strategies II and CC, the comparison of the total profit between these two strategies remains independent of c  (cf. Proposition 2b). Two drivers in particular have an impact on the comparison between strategies II and CC. First, outsourcing to a competitor can minimize competition; second, producing all components in-house can eliminate the inefficiency of double marginalization. When all firms have the same production costs ( 0  c ), Proposition 2a shows that the advantage of mitigating competition by outsourcing to a third-party supplier is smaller than the inefficiency of double marginalization introduced by deploying strategy TT. Consequently, the total profit is larger under strategy II than it would be under strategy TT, regardless of the degree of competition. Proposition 2b extends the result of Proposition 2a by illustrating that, compared with outsourcing to a third-party supplier, outsourcing to a competitor is more effective in mitigating competition. In particular, when competition is sufficiently strong ( 3 13   

) and all firms have the same production costs ( 0  c ), the two OEMs are better off mitigating competition by deploying strategy CC than by deploying strategy II despite incurring the inefficiency of double marginalization. Proposition 2c further illustrates that outsourcing to a competitor does a better job of mitigating competition compared to a strategy of outsourcing to a third-party supplier. Specifically, when the level of competition between the two products is intermediate (e.g., 2 / 1   ), then mitigating competition is marginally valuable, and the total profit under strategy CC is higher as long as the costs of the third-party suppliers are sufficiently high (

62 . 0 ) 2 / 1 ( 1     c
). When the level of competition is intense ( 1   ), mitigating competition becomes very important and, hence, strategy CC is always superior to strategy TT, even when the third-party suppliers costs are extremely low (

      ) ( lim 1 1   c ). It can be shown that ) 0 ( ) 0 ( 3 2 b    (cf.
Propositions 2d-e). Therefore, depending on the relative cost efficiency between the OEMs and the third-party suppliers, when there is no competition, the OEMs would choose between two options: both would outsource to one another or both would outsource to third-party suppliers. When there is no competition, the firms are never better off in deploying the asymmetric strategy (strategy TC). In the presence of competition, however, the comparisons of the total profit under strategies TT, TC and CC are not monotonic in the degree of cooperation between the two competing OEMs. Specifically, Proposition 2d shows that some degree of cooperation between competitors (strategy TC) is better than no cooperation (strategy TT) as long as the third-party suppliers' costs are larger than

) ( 2   . As  increases, ) ( 2  
decreases, hence, a certain degree of cooperation (strategy TC) becomes relatively more valuable when compared to strategy TT because the third-party suppliers must be relatively more cost effective ( ) ( 2   more negative) for strategy TT to be optimal. However, Proposition 2e demonstrates that maximum cooperation (strategy CC) is not always better than some cooperation (strategy TC). When the degree of competition is small, it can be shown that the total profit under maximum cooperation (strategy CC) is larger than what it would be under some degree of cooperation (strategy TC) as long as the third-party suppliers' costs are not too low ( )

( 3  a c    where 0 ) ( 3    a ).
However when competition is strong, we find that TC GS  is larger than CC GS  when the OEMs' costs are similar to those of the third-party suppliers. Therefore, cases do exist where the total profit under strategy TC is larger than it would be under strategy CC in spite of the cost disadvantage of the third-party supplier ( 0

 c

). We present the optimal strategy in Proposition 3. The outside region represents the cases in which no demand of both products is positive. Proposition 2. The optimal outsourcing strategy is presented in Fig. 1.

Fig. 1. Optimal outsourcing strategy.

When being the outsourcee of the competitor (firm S), the OEM (firm G) can have two revenue sources: one from the wholesale market and the other from the end product market. Therefore, when there is no competition between the two competing products ( 0   ), the two OEMs will outsource to one another (strategy CC). However, if the third-party supplier is significantly more cost effective ( 0  c ), they will outsource to the third-party supplier (strategy TT). Previous research has shown that competitors cooperating as supply-chain partners can minimize competition. Therefore, one might expect that when competition intensifies ( increases), the region where strategy CC is optimal will also increase because there is more cooperation under strategy CC than that of strategy TC. We find, however, that this insight might not hold. The total profit of both OEMs consists of two drivers. The first is the relative cost efficiency between the OEMs and the third-party suppliers c  , and, numerically, we observe that this relative production cost has more impact to the total profit under strategy CC ( CC GS  ) than to the total profit under strategy TC ( TC GS  ). The other factor that affects the total profit is the degree of competition between the two products (  ). We find that the total profit under strategy TC is more likely to be larger than it would be under strategies TT and CC when is large (cf. Proposition 2). As a result, one of the firms can be better off outsourcing to the third-party supplier (strategy TC), even when the third-party supplier's production costs are higher than those of the competitor and when the competition is strong (cf. Point X in Fig. 1). Next, we extend our results by altering some assumptions in the model. We assume the difference between third-party supplier and competing OEM is the quality of the components rather than the cost. Under this discussion, the basic framework of the model and the game sequence are similar to that in the previous discussion but the demand function is different. All firms have the same production costs ( 0  c

). i i i i p p N Q D         1 1 1 ) ( ,       T N C N N Q i 1 ) (  (2)
The solution process in this section can be proved in the same way as in the previous solution, respectively, and thus the proofs are omitted for brevity. We just summarize the main findings here. Proposition 3. The optimal outsourcing strategy is presented in Fig. 2 and is expressed mathematically as follows (where  ,  and  are threshold value). When    , the equilibrium strategy is that firm G and firm S both choose to outsource to the third-party suppliers so that they are just pure competitors in the market. It is because when  is small, outsource to the third-party supplier is better.

When     1
, outsourcing to the third-party supplier, OEM will also get the larger profit than outsourcing to the competitor. When

  

, the best choice for both OEMs is outsourcing the ingredient to each other, even in this case OEMs could not price its product aggressively. When compared to the profit of OEMs under others strategies, it shows that both OEMs get the largest profit for outsourcing to each other (strategy CC).

And we consider the case where outsourcing to competitor, the OEM will get high quality end-product (i.e.,

 

). In strategy CT, firm G will get the high quality end-product with outsourcing to firm S and firm S just outsources to the third-party supplier. The demands and the prices of firm G and firm S are better off with the increases of simultaneously in strategy CT. We know when firm G outsources to the competitor firm S; it will manufacture the different quality end-product. With 1   , the quality of firm G's end-product is high. The high quality end-product brings the larger total market demand for firm G and therefore allows firm G to charge a higher price. Since firm G charges a higher price which has the positive impact on firm S's end-product demand even firm S's total market demand is unchanged. So the demand of firm S is also increasing and allows firm S to charge a higher price even its end-product's quality is lower than that of firm G. We also get that the increasing speed of price for firm G is faster than it for firm S (i.e.,

 

     CT S CT G P P
). As a result, the demands and the prices for both OEMs are better off with  increasing in strategy CT (strategy TC).

Next, consider the case where outsourcing to competitor, the OEM will get low quality end-product (i.e.,

 

). In strategy CT, firm G will get the low quality end-product with outsourcing to firm S. We know that the demands and the prices of firm G and firm S are better off with the increases of  simultaneously in strategy CT. With increasing, the total market demand of firm G is increasing even which is less than that of firm S. The increasing total market demand allows firm G to charge a higher price. When firm G charges the higher price, firm S will get larger end-product demand since the price of firm G has the positive impact on firm S's demand and allowing firm S to charge a higher price. And we can know  

Conclusion

In this paper we talk about the outsourcing strategy of two OEMs. Because the cooperation between the competitors could reduce the competition, people think more cooperation is the better. But in the research, we find that since two OEMs have the price competition in the market, one OEM had better outsource to the third-party supplier even if the competitor OEM's production cost is lower. So the cooperation degree is important.

Fig. 2 .

 2 Fig. 2. Optimal Outsourcing Strategy for Different Quality.

  the result that the demands and the prices for both OEMs are better off with  increasing in strategy CT even 1   . Similarly, in strategy TC, the demands and the prices of firm G and firm S are better off with the increasing of  simultaneously in strategy TC. So we know that under strategy CT and strategy TC, the demand and the price of the both OEMs will increase consistently whether

Table 1 .

 1 The profits of firms in different strategies.

	Firm	Strategy TT Strategy TC	Strategy CC
	G (OEM)

We research the supplier selection question with some limitations. First we assume that the information about the production cost is complete information. When the cost is private, we must think about the information sharing or information leakage question. Second, we study the market where the demand of the market is certain. If the demand is uncertain, the firm must think about the inventory problem and leftover problem. These will make the supply chain inefficient.


Yu Changrui

Luo Yan
email: luoyan@shu.edu.cn

Liu Kecheng

A Multi-attribute Collaborative Filtering Recommendation Algorithm Based on Improved Group Decision-making

Keywords: Personalized recommendation, Pearson-Compatibility, Group decision-making, Multi-attribute, Collaborative filtering

The paper builds an evaluation model of user interest based on resource multi-attributes, proposes a modified Pearson-Compatibility multiattribute group decision-making algorithm, and introduces the algorithm to solve the recommendation problem of k-neighbor similar users. Considering the characteristics of collaborative filtering recommendation, the paper addresses the issues on the preference differences of similar users, incomplete values, and advanced converge of the algorithm. Thus the paper realizes multi-attribute collaborative filtering. Finally, the effectiveness of the algorithm is proved by an experiment of collaborative recommendation among multi-users based on virtual environment.

Introduction

The goal of a recommender system is to generate meaningful recommendations to users for items or products that might interest them [START_REF] Adomavicius | Toward the next generation of recommender systems: A survey of the state-of-the-art and possible extensions[END_REF]. In many markets, consumers are faced with a wealth of products and information from which they can choose. To alleviate this problem, many web sites attempt to help users by incorporating a recommender system that provides users with a list of items and/or web pages that are likely to interest them. There are real-world operations of industry strength recommender systems, for example the recommendations for books on Amazon, or movies on Netflix, and so forth.

As one of the most successful approaches to building recommender systems, collaborative filtering (CF) uses the known preferences of a group of users to make recommendations or predictions of the unknown preferences for other users [START_REF] Su | A survey of collaborative filtering techniques[END_REF]. The developers of one of the first recommender systems, Tapestry [1] (other earlier recommendation systems include rule-based recommenders and user-customization), coined the phrase "collaborative filtering (CF)," which has been widely adopted regardless of the facts that recommenders may not explicitly collaborate with recipients and recommendations may suggest particularly interesting items, in addition to indicating those that should be filtered out. The fundamental assumption of CF is that if users X and Y rate n items similarly, or have similar behaviors (e.g., buying, watching, listening), and hence will rate or act on other items similarly.

Literature [START_REF] Goldberg | Using collaborative filtering to weave an information tapestry[END_REF] studies have shown that users' interest to a product or service are affected by user topic preferences, content preferences, user habits, public evaluation and other factors, and these factors is decided by the different attributes of items. For example, the reasons of users liking a new movie may be caused by one or more attributes of the movie, such as the director, star, theme, content, style, public comment and other factors. Thus, in the application of collaborative filtering algorithm, it is necessary to use multi-attribute analysis model, that the user rating to an item should be from a different perspective (attributes) to describe their interests preferences.

However, the current research work of multi-attribute collaborative filtering focus on clustering users and resources based on the attribute information, and the recommended method is still more traditional. Such methods can only obtain a set of potential interest items of target users, but the reasons of such a recommendation is not given to the target user. In addition, the present study do not consider the characteristics differences of similar users interested in the item attributes. It can lead to recommendations deviation. For example, in the traditional way, User B is the most similar to the target user A, because A and B on the same film have the same degree of interest. But if they prefer the film properties are completely different, it will lead to recommendations deviation when we give greater weight given to B used to predict interest preferences of A.

We think that the multi-attribute collaborative filtering can be regarded as a group decision-making process. By building the rating matrix of target items for the similar users, we can remove the user who has a large attribute preference difference with target user from the nearest user set, and save the problem of recommendations deviation. And we can analyze the user's interest performance from the view of item's attributes, give the reasons descriptions for the recommendation. In order to achieve this goal, the paper has proposes a modified Pearson-Compatibility multi-attribute group decision-making algorithm, and introduces the algorithm to solve the recommendation problem of k-neighbor similar users. The organization of the paper is as follows. We review recommender systems and multi-attribute utility theory in Section 2.In section 3, with applied ontology method to describe user profile, we introduce detailed how to build a user interest model. In section 4, we expound the algorithm in each steps specifically. In section 5, an experiment is reported and the findings also be discussed.

Descriptions of Basic Model

A user's comment on a certain item is usually an integration of multi-attribute comments made from different angles. Suppose an item is shown as follows: U . Here we use the 1-9 scale Paired comparison method to analyze the compared importance level of each attribute of the product that a user evaluates [19] . The rating matrix of an item is mainly acquired through user scoring, or acquired through user behavior analysis, or acquired with the approaches of Web semantic Suppose user U j has rated several items and the rating matrix set is 1 2 { ( , ), ( , ),... ( , )} j j j t

P = { 1 2 3

AS A U P A U P A U P 

, where ( , )( 1, 2,... )

j i A U P i t 
is user U j 's rating matrix for product i (i.e., i P ). This paper applies the rating matrix set to establishing the user interest model. The specific steps are as follows.

1. Calculating the feature weight vector of each rating matrix, and then acquire the feature weight vector set V w w w w denotes the feature weight vector of the user rating matrix ( , )( 1, 2,... )

j i A U P i t  and ( ( , )) j i
size A U P denotes the length of the feature weight vector.

2. According to the category of each attribute, calculate the user interest weights of the relavant attribute in the related resource category. Referring to the methods, we propose the following formula for calculating the degree of the user interest.

( , ) ( ) ( , , ) k j n P j k U y k j y A U P V w Va U a n n     (1)
. Va U a n denotes the degree to which user U j is interested in attribute y a . n is the number of the items which has attribute y a and user U j have rated. ( , ) ( )

A U P                                                         1 2 3 1 ...
k j P j k U y A U P V w  (k=1,

Collaborative Filtering Recommendation Algorithm Based On Multi-attribute Group Decision-making

Firstly, we introduce the calculation of the value of user impact weight. This value is an important indicator to measure the degree of evaluation information consistency between a user and the others. The user matrix with higher group evaluation consistency will get higher weight. Vise versa. This paper adopts the concept of user rating similarity [START_REF] Luis | Rueda-MoralesCombining content-based and collaborative recommendations: A hybrid approach based on Bayesian networks[END_REF]7]. We turn all the similar nn  user rating matrixes into 2

k k k k k k k k k k k n n n nn V           
. Pearson similarity formula to calculate the rating matrix between the user U k and the user U l is show as follows:

(2) k V is the average value of all elements of user U k rating matrix.

. The similarity between user k and other users could be calculated as follows:

( ( ) ) ( ( ) ) ( , ) [ ( ) ] [ ( ) ] n k k l l k l i n n k k l l u u V i V V i V Si A A V i V V i V              11 12 2 ... k k k k nn V n        * 1 1 / p p k ij k ij k k k           (4) Matrix * A is not a positive reciprocal matrix. Suppose X is a positive reciprocal matrix composed of ij x
. This paper uses the least square method to modify X and propose the following formula:

*2 1 1 ( ) min ( ) n n ij ij i j F X x       1 . 0 ( , 1, 2,... ) ij ji ij xx st x i j n       
(5) There are two important indicators in this article: compatibility and comprehensive compatibility. Their definitions are as follows:

Definition 1: Suppose X is the group user comprehensive evaluation matrix obtained by using the method of the least squares. Then the judgment matrix compatibility between user k and the other users is as follows:

() ( ) 2 2 1 1 2 max(( ) , ( ) ) ( , ) k n n ij ij k i j ij ij k x x S A X n          (6)
Although paper [25] has defined expert judgment matrix compatibility in usual cases, it does not consider the incomplete value. Formula [START_REF] Luis | Rueda-MoralesCombining content-based and collaborative recommendations: A hybrid approach based on Bayesian networks[END_REF] is a modified approach to solve the problem. Firstly, the block that the user does not rate is processed and given the value 0. Then  is used to indicate the number of 0. The aim of this approach is to eliminate the influence of user judgment matrix on compatibility indicator.

Definition 2 : Suppose , evaluation matrixes of all p similar users is considered compatible.

Experimentation

In order to validate the effectiveness of this algorithm, we build an experiment environment to execute our algorithm at current conditions. The environment is described as follows:

Ontology and the relevant methods are adopted to design and develop the movie information database. Jena 2.6.2 is applied to store the movie information in RDF format and ARQ-2.2 is used to manage the movie information. We have imported 300 movies which involve 10 categories. A semantic analysis of each movie is conducted to get key words and form the initial attribute set. Then the synonyms and the similar words in the initial set are combined. Take some topical words as the characteristic attributes and use them to represent these movies. Finally, 15 attributive categories and 282 concrete attributes are extracted. Then an online multi-attribute rating system based on the movie database and a collaborative filtering recommendation system based on group-decision making are designed and developed.

The concrete process that tests the algorithm is as follows:

1. Select four evaluated movies in which ( , ) G u p is comparatively big and use them as the testify set. They respectively include 6,7,8 and 9 attributes. Then, use the target user evaluation matrixes which are further used as the real weight vectors to calculate the user interest vectors for each movie.

2. Based on the user-evaluated movies set (excluding the 4 movies in the test set), apply the methods in sections 2.3 and 2.4 to searching the most similar user set for the target user (i.e., the similar interest distributions).

Take Movie 1 with 6 attributes as an example. The real interest vectors are S=[3.7288, 2.7053, 1.9627, 0.4657, 0.3293, 0.3293]. The total score of this movie is 4.5 which indicates that the target user has a high preference to this movie. Moreover, the preference is mainly determined by the first three attributes. Totally, 9 similar users have evaluated this movie. Firstly, the traditional collaborative filtering algorithm is applied to obtaining the weighted average of the total score of this movie and gets the result 3.94. We are not sure whether the target users have interests in this movie. Thus, we need use the similar user evaluation matrixes to make judgments. The evaluation matrixes of six similar users are listed as follows:

1 2 2 5 7 9 1/ 2 1 1 6 5 6 1/ 2 1 1 7 7 6 1/ 5 1/ 6 1/ 7 1 1 2 1/ 7 1/ 5 1/ 7 1 1 3 1/ 9 1/ 6 1/ 6 1/ 2 1/ 3 1 A                    1 2 3 3 7 7 1/ 2 1 0 4 7 6 1/ 3 0 1 0 4 5 1/ 3 1/ 4 0 1 1 2 1/ 7 1/ 7 1/ 4 1 1 3 1/ 7 1/ 6 1/ 5 1/ 2 1/ 3 1 B                    1 2 2 4 0 0 1/ 2 1 1 3 0 0 1/ 2 1 1 2 0 0 1/ 4 1/ 3 1/ 2 1 0 0 0 0 0 0 1 0 0 0 0 0 0 1 C                    1 2 0 7 8 0 1/ 2 1 2 8 7 7 0 1/ 2 1 0 7 8 1/ 7 1 0 1 1 2 1/ 8 1/ 7 1/ 7 1 1 1 0 1/ 7 1/ 8 1/ 2 1 1 E                    1 2 3 4 6 8 1/ 2 1 2 2 4 6 1/ 3 1/ 2 1 1/ 2 4 7 1/ 4 1/ 2 2 1 3 2 1/ 6 1/ 4 1/ 4 1/ 3 1 2 1/ 8 1/ 6 1/ 7 1/ 2 1/ 2 1 D                    1 2 4 0 7 0 1/ 2 1 2 6 7 7 1/ 4 1/ 2 1 0 2 2 0 1/ 6 0 1 0 2 1/ 7 1/ 7 1/ 2 0 1 1 0 1/ 7 1/ 2 1/ 2 1 1 F                   
(3) Use the following four algorithms to calculate the score of the four movies and make comparisons on the deviations of the real weight vectors of the target users. The result is listed as follows:

Table 1 1, when the scores of a part of similar users have a large deviation from those of the other users, the algorithm proposed in this paper can solve the problem of early convergence better than the other algorithms and obtain an accurate result. The core of our algorithm is the revised values of the comprehensive evaluation matrix determined by the majority of users. On this basis, the highly deviated evaluation values are revised. The result of seven order matrix experiment shows that the deviations of the result of any algorithms are not notable when all the similar users have unanimous evaluation matrixes,. The result of nine order matrix experiment shows that the result of the proposed algorithm is similar to that of compatibility correction algorithm when all the similar users have unanimous evaluation matrixes, while still have some incomplete values, and is better than the other two algorithms obviously. When there are 5 similar users and six order evaluation matrix is executed/ implemented with our algorithm, the change tendencies of the main indicators are shown in Figure 1: (4) Take movie one with the six order evaluation matrix as an example. The influence of the user number on the accuracy of recommendation results is examined. Suppose the user number is 3,5,7 and 9. The accuracy and the number of iterations are calculated with different means of permutation and combination. Part of the result is shown in Table 2 When the user number increase, more deviation items are generated. Thus, the iterations of this algorithm raise. This test indicates that the effectiveness of this algorithm is highly related to the initial consistency degrees of all users and the number of users. In general, when the initial consistency degree is low and the similar user set is limited (e.g. there are 3 users), it is hard for the algorithm to dig out the common information among the users. Therefore, the result deviation is huge. However, when the number of similar users increases to a certain degree (e.g. the number is equal or bigger than 7), this algorithm still remains a good accuracy, even if the initial compatibility is low.

In the aspect of providing personalized services to the target users, this paper calculates the comprehensive evaluation weight vectors of each movie with the groupdecision making model. Take movie one with 6 attributes as example. The comprehensive evaluation score of nine similar users is ( , ) G u p =3.94. The comprehensive evaluation vectors are V= [4.0653 2.9492 1.7630 0.3972 0.3044 0.3607]. Each value of the weight vector represents the potential interest degree of the target user on the corresponding product attributes. Thus, the total score calculation formula is 1 ( , ) /

n i i TScore G u p V n     (10)
where TScore denotes the total score, n denotes the number of attributes, and i V denotes the comprehensive evaluation value of the (i)th attribute of the product. The recommendation set can be fixed through the way of ranking or threshold setting. The total scores of the four movies is shown in Figure 2: As shown in Figure 2, the movie one has the highest score with six attributes. The characters of this movie are analyzed as follows using the user interest model. Firstly, attribute i V of weight vector V is normalized and generate vector [0.4131, 0.2997, 0.1792, 0.0404, 0.0309, 0.0367] V 

. The three attributes whose values are bigger than the average value 0.1666 are picked out. When the attribute value is bigger than 0.1666, the majority of users have evident preference on movie one. In the target user interest model, there are 124 attributes totally. The 3 attributes of movie one that are bigger than the average value are connotation, characteristic and Fig. 2 The TScore of four movies special efficiency are still larger than the average value (1/124=0.0081) among 124 target user attribute preferences. This result indicates that target user has evident preference to these 3 attributes and the popularity of this movie is mainly determined by these attributes. Therefore, we could introduce movie one to target user and provide the reasons why this movie is introduced. We also could use semantic

Conclusion

Currently, the collaborative filtering personal recommended algorithm lesser consider the multi-attribute problem. We take the method which is based on the group-decision making, then build an improved Pearson-Compatibility algorithm and apply it into the collaborative filtering recommend field. And we also build a virtual recommend environment and testify the effectiveness and feasibility of this algorithm. The collaborative filtering personal recommended algorithm which is based on groupdecision making have some advantages as following: Firstly, we could find a more suited similar users set for the target user. Through field subdivision based on field attributes, we could get a more accurate target user model. Take the model as a foundation, we could find users who have similar interest distribution with target user and build the similar users set.

Secondly, our algorithm could provide more accurate and personal recommend service to our target user. The traditional collaborative filtering method merely could recommend a result set to target user, but could not provide analysis service. Our method overcome this weakness, making a information integration to know what are mainly factors determining the user preference, so that we could handle the user need more accurate.

Thirdly, we consider evaluation deviation between the similar users and revised the user evaluation. Compared with the traditional method of weighted mean, we use group-decision making method to calculate the comprehensive evaluation score. We believe deleting the deviation item and revising the evaluation matrix could make the result have a better fitting effect. We also applied the collaborative filtering method which is based on Pearson-Compatibility to the personal recommended field. The experiment result shown that this algorithm is stable when facing the deviation items and could find the common preference information between similar users.

If we want to apply this algorithm to real business environment, we need a new user evaluation model as a foundation. For there are little related research currently, the next step is to apply internet technique to build user online evaluation system. After collecting lots of user data, we could research this model further to testify its effective.

1

 1

  p denotes the number of users. We propose a formula 1 measure of variance, which indicates the deviation degree of evaluation matrix. The approximate influence weight of user U k is shown as follows: After acquiring the similar user influence weight, we suppose the group integrated approximate evaluation matrix of p users is * A , and the value of each element * ij 

A

  are the compatibility correction of matrixes of p users' judgment matrixes. Then we get the comprehensive consistency indicator _ S , as follows: Readers can refer to the simulation result of article [19]. When S(A,B)>= 0.8, the two evaluation matrixes is considered nearly compatible. When _ 0.8 S 

Fig. 1 :

 1 Fig.1: The main indicators change in our example 0 2 4 6 8 10 12 14 16 0.65 0.7

Fig. 3 :

 3 Fig. 3: Comparison analysis histogram of movie one with target user interest.

  analysis technique to describe each attribute in detail and provide more personal service to target user. The comparison analysis histogram is shown in Fig.3.


Xiao Yu

Han Jingti

Value Model of Knowledge Diffusion in High Technology Innovation Networks

Keywords: High-tech innovation networks, Social learning, Knowledge diffusion, Diffusion value, Degree distribution

To measure the influence of knowledge diffusion and information exchange between enterprises on high-tech innovation networks' production and operation, a production model and innovation model based on a network diffusion process are introduced. The knowledge diffusion process treated as an learning-by-observing process in a random network are influenced both by network's structure and non-structure properties. We analyze the influence of diffusion process theoretically and find that, if given the precondition that initial belief and belief elasticity follow a normal distribution, an increase in mean of initial belief would lead to increase in PV and IV; otherwise, mean of belief elasticity would have a opposite effect on PV and IV under some different conditions. Finally, we give the condition to compare knowledge diffusions in two high-tech networks with same mean degree but different variance.

Introduction

For high-tech enterprises, continuous technological innovation is one of key factors to maintain their core competitiveness. Technological innovation depends largely on the partnerships they coordinate with other companies within the industry [START_REF] Corey | Knowledge, networks, and knowledge networks: a review and research agenda[END_REF]. As a substrate of knowledge transferring between high-tech enterprises, high-tech innovation network has an important impact on the dissemination process of knowledge between enterprises [START_REF] Uzzi | Collaboration and creativity: the small world problem[J][END_REF]. For example, [START_REF] Uzzi | Collaboration and creativity: the small world problem[J][END_REF] found that large-scale structure of the network has a significant effect on musicians' creativity, art of music composed and profitability [START_REF] Schilling | Inter-firm Collaboration Networks: The Impact of Large-Scale Network Structure on Firm Innovation[END_REF]. [START_REF] Schilling | Inter-firm Collaboration Networks: The Impact of Large-Scale Network Structure on Firm Innovation[END_REF] found that structure of inter-enterprise cooperation networks influence their knowledge creation and that enterprises cooperative networks in which have a higher clustering coefficient and less redundant links would have higher knowledge creation capability [START_REF] Corey | A longitudinal study of the influence of alliance network structure and composition on firm exploratory innovation[END_REF]. Phelps (2010) found that structure and composition of alliance networks have a significant impact on the ability of enterprises to explore innovation, corporate partners' technical diversity has a positive influence on its ability to explore new knowledge, while the density of network structure plays as regulatory role [START_REF] Newman | The structure and function of complex networks[END_REF]. These empirical findings provide new reference for companies to acquire knowledge and set off a wave of knowledge diffusion research of high-tech innovative networks.

With above empirical findings and the rise of complex network studies [START_REF] Barabasi | Emergence of scaling in random networks[END_REF][7] [START_REF] Enyu | A network model of knowledge accumulation through diffusion and upgrade[END_REF], a large number of scholars have focused on dynamic characteristics of large-scale network structure and knowledge emergence properties in these networks. [START_REF] Cowan | Network structure and the diffusion of knowledge[J][END_REF] examined the relationship between network structure and diffusion performance, and concluded that knowledge diffusion in small-world networks with different structure properties have great difference [START_REF] Cowan | Network structure and the diffusion of knowledge[J][END_REF]. These studies have used simulation to explore knowledge diffusion process, but did not analyze the impact of such knowledge diffusion on the overall economy of high-tech innovation networks. In this paper, we define knowledge as a group of technical knowledge which would have a positive impact on production, operation and business activities (or reduce production costs and improve product quality). Although technical cooperation network can accumulate knowledge and innovate technologies, thus increase the whole profit, the implementation of these activities may bring risks and costs, so companies will evaluate the risk and decide whether to absorb new knowledge or not. Based on this consideration, this paper, which is different from previous studies, treat knowledge dissemination process as a learning process, companies will observe business-to-business alliances' adoption of new knowledge in return each period, and then evaluate the impact of new knowledge on their production and management, and finally decide whether to adopt new knowledge.

We build high-tech innovation network with mean-field approximation [START_REF] Jackson | Diffusion of behavior and equilibrium properties in network games[END_REF][11] [START_REF] Lopez | Influence networks[END_REF], with the aim to discuss the heterogeneity of degree distribution on knowledge diffusion. In addition, this article focuses on the overall impact of knowledge diffusion on the high-tech innovation network. Based on knowledge diffusion models, we also construct knowledge diffusion production value model and inter-enterprises impact value model. This article focuses on answering these questions: How is the impact of cost and actual knowledge value on adoption rate, production value (short for PV ) and influence value (short for IV) in high-tech innovation network? How is the impact of initial belief of knowledge value and elasticity of evaluating of knowledge value on adoption rate, PV and IV? What about the heterogeneity of degree distribution's regulated role in these relationships?

Assumptions and Individual-level Model

We regard knowledge dissemination process in the high-tech innovation network as a process of enterprises' evaluating benefit of adopting new knowledge. The transmission of knowledge isn't the same that of information or viral, but is an active behavior of knowledge acceptors. In this process, high-tech enterprises evaluate knowledge value repeatedly, combine together adoption cost and adoption risk of knowledge, and then decide whether to adopt new knowledge or not.

To simplify the process, we firstly make the following assumptions: 1) each period enterprise i can observe new knowledge adoption behavior of other k i enterprises, a i of which have adopted new knowledge; 2) For those enterprises who have adopted new knowledge, they produce a unit of information about the adoption of return every period which are independent from each other, the value return follows a normal distribution I ~N(m,s 2 ) ; 3) Enterprise has a neutral risk preferences, information obtained in each period is independent with each other; 4) enterprises have the same cost c and value return of using knowledge; 5)the diffusion process follows a mean-field approximation process.

High-tech Innovation Networks

The high-tech innovation network is represented as

DG = V, E ( ) .
The enterprises in the network are represented as node set ( ) , so the degree distribution could be represented as

V = 1, 2,..., n { } ,
P k ( ) = p(k);k = 1, 2,...., k max { } .

Knowledge Adoption Rules Based on Learning from Observation

If enterprises have complete information of knowledge value, and m > c , then all would adopt the knowledge at the initial stage. In reality, Enterprises actually don't, however, know that kind of information. But they have a prior belief of the knowledge value, which is represented as v i 0 . With the accumulation of information about knowledge value, they would also update their belief about knowledge value.

When the posterior belief b it at stage t is strong enough, enterprise i would decide to adopt the knowledge. Here we use a standard normal-normal belief updating rule [16][17] . At the initial stage, enterprise i 's initial belief of knowledge value is v i 0 . A low v i 0 means that enterprise i has a negative attitude towards that knowledge. t i represents the elasticity of enterprise i 's belief updating. As time goes, enterprises get information from its neighbors, and then update their belief. At t period, if there are a i adopted neighbors, enterprise i would obtain a i units of information. For that information is independent from each other, we can get the mean of knowledge value I it ~N(m,

s 2 a it
) . Followed as standard norm-norm updating rules, enterprise i 's bayesian posterior belief can be expressed as the weighted average of prior valuation and the information obtained

v it = t i a it + t i × v i0 + a it a it + t i × I it
When v it > c , enterprise i will adopt the knowledge.

A knowledge Diffusion Dynamic Model in Random Network

Each stage, suppose that enterprise i selects k i enterprises randomly from the network to get information about knowledge usage. In the initial period, enterprises who meet the condition v i0 > c would adopt knowledge and then create information for others next stages. Meanwhile, those adopted enterprises would, if their belief of knowledge value is larger than cost, give up using new knowledge.

We use r k (t) to represent knowledge adoption rate of sub-class featured with degree k in t period, and r(t) to represent knowledge adoption rate of innovation network. Obviously, we have

r(t) = r k (t)p(k) k å (1)
in which 0 £ r k (t), r(t) £ 1 . Since enterprises may get several other enterprises' information, the probability of enterprise's observing an adopted counterpart is represented as

q(t) = kp(k)r k (t) k å k (2) in which k = kp(k) k å
is mean degree of the network. Thus, expected amount of information of enterprise i could be represented as

I k (q(t)) = C k a q(t) a (1-q(t)) k-a a a=0 k å = k ×q(t) (3)
For enterprise i , the bayesian posterior belief of knowledge value at stage t is

v it = t i k i q(t) + t i × v i0 + k i q(t) k i q(t) + t i × I it - (4)
, in which

I it - ∼ N(m, s 2 k i q(t)
) . When v it > c , enterprise i would adopt knowledge, and then we have

t i v i0 + k i q(t)× I it - k i q(t) + t i > c . Suppose X ~N(0,1), then (t i v i0 + k i q(t))× m + (t i v i0 + k i q(t))s k i q(t) × X > (k i q(t) + t i )×c which implies q(t) > t i (C -mv i0 ) k i (m -C) - (t i v i0 + k i q(t))s (m -C)× k i q(t)
× X

Now we can define the expected information level for enterprise i adopting knowledge

q i = t i (c -mv i0 ) k i (m -c) (5)
when q(t) > q i ,enterprise i would be inclined to adopt knowledge. Without lose of generality, suppose that when q(t) > q i ,enterprise i will adopt knowledge.

For sub-class k , we have:

Z(t i , v i0 ;k) = t i (c -mv i0 ) k(m -c) (6)
Suppose it's a continuous process, then the growth of adoption rate of sub-class

k could be characterized as r k (t) i = F(q(t);t i , v i0 , k) -r k (t) (7) q(t) i = R(q(t)) -q(t) (8) r(t)
i = p(k)F(q(t);t i , v i0 , k) k å -r(t) (9) R(q(t)) = kp(k)F(q(t);t i , v i0 , k) k å k ( 10
)
When R q t ( ) ( ) = q t ( ), equilibrium state is reached. Recall that the transitive from adopted to non-adopted is always possible. Hence, the equilibrium only refers to the value of r(t) and q t ( ) .

Knowledge Diffusion Value for High-tech Innovation Network

modeling knowledge dynamics in high-tech innovation network, a following question would come as 'How is this diffusion's impact on the whole high-tech network?', and 'what's the role of the interaction between enterprises?'. Most literatures didn't, however, discuss these questions. For an analytical purpose, we construct a PV model and IV model, which is similar as customer value and influence value in Ho(2012). PV measures accumulated value from all enterprises' using new knowledge, and IV measures value raised from the interaction among enterprises, both of which are influenced by network structure.

Firstly we introduce discount coefficient r and valid period of knowledge. For a particular adopted enterprise, suppose that its PV each period is equal to m . If enterprise i adopts knowledge at t , its PV could be represented as

PV i = me -rs s=t T å (11)
Except adopters in the initial stage, all new added adopters are influenced by earlier adopters whose influences are related to their degree. Thus, accumulated IV of adopted enterprise i with degree k i could be represented as

IV i = w k i n × k q(s) × n × r(s) i × me -rs t T ò ds = k i wm k r(s) i × e -rs
q(s)

ds t T ò ( 12
)
in which 0 < w <1 and t ³ 1 . w is coefficient of IV. Thus, PV and IV of knowledge in its life cycle could be represented as

PV = nm e -rs r(s) i t T ò 0 T ò dsdt ( 13
)
IV = nmw k e -rt r(t) i dt 0 T ò (14)
PV characterizes the direct influence of knowledge on enterprises' production and operation, and is also influenced by the shape of knowledge adoption curve. Obviously, for two knowledge diffusion processes who have the same final adoption rate, the one having a higher initial adoption rate would also have a larger PV and a smaller IV. On the contrary, the rate of IV to PV mirrors the characteristics of diffusion curve. In this view, knowledge payoff, cost, enterprises' characteristics, and structure of high-tech innovation network all influence the shape of diffusion curve, would in turn influence PV and IV.

Theoretical Analysis

The diffusion process is influenced not only by knowledge payoff and cost, but also by distribution of initial belief b 0 and of elasticity t and by degree distribution P(k) . If max{v 1,0 , v 2,0 ,..., v i,0 ,..., v n,0 } < c , all enterprises would not adopt that knowledge at initial stage, which in turn lead to no information for next stage and finally to the failure of knowledge diffusion. This phenomenon could be found in practice-some technologies or knowledge are too expensive or revolutionary, giving enterprises a sense of risk, and thus all enterprises are waiting. As for this case, if m > c, the creator of knowledge could take some measures to handle with, such as giving discount to initial adopted enterprises or signing an agreement about sharing risky. Then, we have proposition 1. Proposition 1: If all enterprises' initial belief of knowledge payoffs are lower than cost, or actual knowledge payoff is lower that cost, knowledge would fail to diffusion in the high-tech network.

For the sake of deeply understanding the model, we suppose that v 0 and t follow a normal distribution. And also we will take the discrete version of this model.

Proposition 2: Suppose v 0 ∼ N(m 1 ,s 1 2 ) and t ~N(m 2 ,s 2
2 ), then 1) an increase in m

1 would lead to increase in PV and IV, 2) when c < bm , an increase in m 2 would lead to increase in PV and IV; when c > bm , an increase in m 2 leads to decrease in PV and IV. Proof. According with equation ( 6)

Z(t i , v i0 ;k) = c -mv i0 k(m -c) ×t i suppose t i = t = 0 , then Z ∼ N( t (c -m × m 1 ) k(m -c) , tm k(m -c) ×s 1 2
) , combining with equation ( 7), we get

q(t +1) = kp(k) k å F k(m -c)q(t) -ct + tmm 1 ) s 1 ktm(m -c) ae è ç ö ø ÷ k in which F(×) is CDF of normal distribution.
Obviously, an increase in m 1 leads to an increase in knowledge diffusion speed. If the diffusion achieve dynamic equilibrium at q * , then

q * = kp(k) k å F k(m -c)q * -ct + tmm 1 ) s 1 ktm(m -c) ae è ç ö ø ÷ k If there is
q * , at this point, an increase in m 1 , the equilibrium state would be disrupted. Thus, would gain a growth. Combining equations ( 13) and ( 14), we can achieve proposition 1). ) , combined with equation ( 7), we get F(q, k) is, for any q , a non-decreasing function of k , then there is more PV and IV in network a than that in network b.

q(t +1) = kp(k) k å F k(m -c)q(t) -(c -mb)m
2) If P a (k) SOSD P b (k) and F(q, k) is, for any q , a weak concave function of k , then there is more PV and IV in network a than that in network b.

Proof.

The proposition is easy to achieve with definition of First Order Statistical Dominance (short for FOSD) and Second Order Statistical Dominance (short for SOSD) given by Jackson (2006) T ® ¥ , time axis would, for a knowledge diffusion process with a low discount coefficient, have little effect on PV and IV. In this situation, the equilibrium state of knowledge diffusion would have a decisive influence on PV and IV. If, however, a knowledge diffusion process is highly risky, which implies there is a large discount coefficient, then the initial stage of diffusion is vital to determine PV and IV.

Conclusion

Knowledge diffusion in high-tech networks is of importance in research area of knowledge management. We construct a knowledge dynamic diffusion model in hightech networks with degree distribution based on observation learning and mean-field approximate method. In this high-tech network, every enterprise observes fixed amount of knowledge payoff from others, and then updates its belief of knowledge payoff. With mean-field approximation, we elicit a threshold of information level for each enterprise to decide whether to adopt new knowledge or not. Besides, we construct production value model of knowledge for high-tech networks and influence value model of the interaction between enterprises.

The result shows that new knowledge would, if all enterprises have too low belief of knowledge value, fail to diffusion in high-tech networks. If given precondition that initial belief and belief elasticity follow a normal distribution, an increase in mean of initial belief would lead to increase in PV and IV; otherwise, mean of belief elasticity would pose an opposite effect on PV and IV under different conditions. Finally, we give the condition to compare knowledge diffusions in two high-tech networks with same mean degree but different variance. The research in future would construct multiple-agents model to analyze different properties of high-tech on knowledge diffusion, and also on PV and IV.

e ij = 1doesn't mean e ji = 1 .

 1 To facilitate the analysis, we assume in_degree equals to out_degree. The proportion of node with degree k in the network is represented as p k

kProposition 3 :

 3 Then we can achieve proposition 2) with the same method used for proposition 1).As in proposition 1, distributions of b 0 and t have an impact on diffusion curve. b 0 's influence is intuitive, t 's is, however, depend on different constrains. If there are two high-tech networks: network a with degree distribution P a (k) depicted by mean

  networks with same average degree and different degree variance, the one with larger variance would also have larger PV and IV. For example, in a scale-free network and a regular network, PV and IV in the former one are larger that those in the later one.The discount effect of knowledge also has an important influence on PV and IV. If

	. P a (k) FOSD P we can conclude that in two random network a and b, if average degree of a is larger k a > k b , then than that of b, there would be also a larger PV and IV in high-tech network a. P b (k) means that a (k) SOSD P b (k) means that k a ³ k b and s a 2 > s b 2 , and we can draw the conclusion that in two

Acknowledgments. This research is supported by projects: National Natural Science Foundation of China (71271126); research Fund for the Doctoral Program of Higher Education (20120078110002); the 6th graduate innovative fund of Shanghai University of Finance and Economics (CXJJ-2012-427); the 4th stage of 211 project of Shanghai University of Finance and Economics.


Seung-Hwa Chung
email: schung@biblesocieties.org

Simon Robertson

Andre Minnaar

Mark Cook

Lily Sun
email: lily.sun@reading.ac.uk

Developing a Knowledge Management System Using an Ontological Approach in Global Organization

Keywords: Knowledge management system, Knowledge model, Ontology, Global organization

This paper introduces an ontology-based knowledge model for knowledge management. This model can facilitate knowledge discovery that provides users with insight for decision making. The users requiring the insight normally play different roles with different requirements in an organization. To meet the requirements, insights are created by purposely aggregated transactional data. This involves a semantic data integration process. In this paper, we present a knowledge management system which is capable of representing knowledge requirements in a domain context and enabling the semantic data integration through ontology modeling. The knowledge domain context of United Bible Societies is used to illustrate the features of the knowledge management capabilities.

Introduction

Knowledge management acquires information from multiple resources and uses the acquired information to generate value for an organization [START_REF] Havens | Knowledge management system and method[END_REF]. For a knowledge management system, there is a need to integrate an increasing set of diverse devices and information systems within an enterprise [START_REF] Rho | Ontology: The key of Internet Evolution, from Web 2.0 to Web 3.0[END_REF], e.g. inventory tracking systems, sales systems, finance systems, human resource systems, and so on, to effectively share the organizational knowledge [START_REF] Davenport | Saving IT's Soul: Human-Centered Information Management[END_REF]. Typically these devices and information systems are produced by a variety of developers with different data schemas. The information integration of such heterogeneous data using traditional data integration [START_REF] Madhavan | Web-scale Data Integration: You can only afford to pay as you go[END_REF], which offers uniform access to a set of data sources through a mediated schema [START_REF] Chai | Analyzing and Revising Data Integration Schemas to improve their matchability[END_REF], is a specialist and brittle process, such that changing the structure of just one data source can force an integration redesign [START_REF] Bernstein | Information Integration in the Enterprise[END_REF]. Data integration without involving semantics limits the data use to pre-defined functions, which are implemented by a rigorously agreed specification with the pre-defined meaning of terms for the exchange of data. This makes it difficult to manipulate the data according to the users' needs and to extract reasoned information from the data. Adding semantics to the data avoids these difficulties [START_REF] Waters | Global Interoperability Using Semantics, Standards, Science and Technology (GIS3T)[END_REF]. OWL (Web Ontology Language) is a well-known technology that enables semantics to be encoded with the data [START_REF] Antoniou | Web Ontology Language: OWL[END_REF]. This technology formally represents the meaning involved in information (in other words, an ontology that specifies concepts and describes relationships between things and categories of things). The ontological approach to build a knowledge model offers significant advantages on sharing and generating value of the information for it enables the reasoning over data and handles the data exchange and integration for more flexibly.

There have been several investigations in this research field [START_REF] Noy | Semantic Integration: a survey of ontology-based approaches[END_REF]. The data integration research trend is moving toward semantic data integration using ontology due to it having the capability of specifying relationships between concepts and facilitating reasoning [START_REF] Gardner | Ontologies and Semantic Data Integration[END_REF]. In industrial practices, major information service enterprises such as Google, Yahoo and Microsoft have agreed on shared vocabularies with encoded semantics to publish the data on the Web [START_REF]Linked Data Community: Supporting Schema.org deployment and usage with a special focus on Linked Data[END_REF]. This work surveyed existing industrial practices of knowledge management system developed using the ontological approach. We were, however, unable to find examples of best practice, which describe an empirical usage of knowledge management systems with a practical industrial dataset, to refer to for the development of the knowledge management system in our organization. However, it is understandable that industries treat their knowledge models and management systems as intellectual property [START_REF] Castle | Knowledge Management and the Contextualisation of Intellectual Property Rights in Innovation Systems[END_REF] as they are important assets that help provide a competitive edge.

This paper introduces an ontology-based knowledge model and the core features of the proposed knowledge management system. The United Bible Societies want such a system to assist with information and service provision.

2

The knowledge domain context

United Bible Societies (UBS) [START_REF]The United Bible Societies[END_REF] is one of the world's largest Christian ministries. UBS is made up of 146 Bible Societies operating in more than 200 countries and territories. They translate, publish, and distribute the Bible, help people engage with content of the Bible, and are also active in areas such as literacy training, HIV/AIDS education and trauma healing. Bible Societies work in partnership and cooperation with all Christian churches and with some non-governmental organizations. Bible Societies' activity is expressed in over a thousand projects to facilitate the funding of these activities. A Global Mission Team (GMT) supports Bible Societies by helping them work together through consultation, coordination and the provision of shared services in areas such as international finance, project registration, capacity building, etc. The organizational structure of UBS is shown in Fig. 1. UBS is multi-cultural and multi-lingual, working in a geographically dispersed and collaborative way across the world -together these attributes make UBS a unique organization.

Information and information sources

This work analyzes the information systems in UBS. There include DBL (Digital Bible Library) system, UBS Community system, TMS (Translation Management System), Salesforce system, Project Registry system, and PSFinancials system. Fig. 2 shows the overview of the GMT core systems, and Table 1 shows a list of the existing information systems with the roles.

Each system is supporting Bible Societies in different domains. For example, DBL and TMS in the Bible domain, Project Registry in the Project domain, PSFinancials in the Financial UBS Community in the Community domain, and HR.net and Salesforce in the Human resource domain. Currently, most of the data is searchable only vertically, which means that the information is only available within the domain [START_REF] Shettar | A Vertical Search Engine-Based On Domain Classifier[END_REF]. Due to the independent design of the different systems, there is only limited syntactically integrated and shared data between the systems. As these systems evolve, it becomes increasingly difficult to extend and share the data. For example, Bible translation information in TMS system cannot be shared with the project information in the Project Registry system. Currently, the user needs to vertically search each domain manually to relate the search results from one domain to another. Integrating data syntactically, to enable a horizontal search to share the information between different domains, requires redesigning the system each time there is a need for new information sharing, and this does not allow for easy evolution.

Fig. 2. Overview of GMT's core information systems

Table 1. List of GMT's core information systems and roles

Design of the Knowledge Management System

Fig. 3 depicts the ontology-based knowledge management system (KMS) which enables creation of insights by processing the transactional data from the resources such as existing information systems. Each resource requires one plugin component, so when there is a need to import new resource, this can be handled by simply adding an additional plugin component. In this architecture, the system can easily evolve to incorporate the new imported information.

Fig. 3. The architecture of knowledge management system

The information from each domain will be aggregated and molded into the ontology model. This model dynamically generates the knowledge presentation to facilitate the discovery of new insight. Fig. 4 shows an example of the knowledge model developed to enable the sharing of information from different domains.

The requirements for the knowledge model for the UBS have been identified as followings.

1

. The model needs to import data from different sources:

The ontology-based knowledge model can aggregate the information from different domains (even from outside the UBS domain, such as the CIA World Factbook [15] and UN Human Development Reports [16]) in one ontology knowledge model.

The model needs to keep relationships between entities:

The ontology-based knowledge model can formally describe the semantics of classes (general things in domains of interest), properties (attributes those things may have) and relationships that can exist among things [START_REF]OWL Web Ontology Language Use Cases and Requirements[END_REF].

The model needs to generate different knowledge presentations:

The ontology-based knowledge model can generate dynamically the knowledge presentations according to each entity defined in the ontology without altering the knowledge model [START_REF] Katifori | Ontology Visualization Methods -A Survey[END_REF].

The model needs to be applied by certain rules to enable the semantic search:

The ontology-based knowledge model can be semantically searched by query syntax such as SPARQL query [START_REF]SPARQL Query Language for RDF[END_REF] or SWRL rule [START_REF]SWRL: A Semantic Web Rule Language Combining OWL and RuleML[END_REF]. For example, show Bible Societies making grants of more than $100,000 to literacy projects which are implemented in Swahili-speaking countries in Africa. From the knowledge model we have proposed, the following interaction can be enabled, as an example. Fig. 5 shows the information details of each or group of entities in the model. 1. A Bible Society (BS) wants to make a funding grant to a literacy project. However, it does not know which projects or countries have the greatest needs.

Conclusion

This paper has described the process of developing a Knowledge Management System (KMS) in UBS that takes the ontological approach, using an ontology-based knowledge model. This model aims to enable horizontal searches, not just vertical searches within a domain, and information sharing between different data sources in different domains. The KMS, using an ontology-based knowledge model, will shape the information into knowledge by aggregating, molding, linking and reasoning with the data. This will facilitate the discovery of new insights by revealing implicit information, which could be hidden without applying semantics to the data. For the future work, we will further design the technical functions in the KMS and deploy the KMS in the day-to-day operations of UBS. Rigorous data collection and processing will be undertaken, followed by rigorous validation of the knowledge model. A successful implementation will also require the consideration of issues related to user motivation and change management.

Fig. 1 .

 1 Fig. 1. The structure of the United Bible Societies

Fig. 4 .

 4 Fig. 4. The knowledge model using heterogeneous information resources

2 .

 2 The Knowledge Management System generates a literacy oriented knowledge presentation from the knowledge model. In the figure, Projects are the first information related to the literacy. The knowledge representation needs to be customized by user requirements. For example, Countries can be the first information to be related to the literacy if they are more appropriate for the user.3. BS receives summary information (e.g. total grant requests/payments) of existing literacy projects and details of each project as shown in the left side of figure. 4. Country and Bible Society profiles (e.g. literacy rate and population) are presented along with the projects as shown in the right side of figure. 5. BS discovers which projects require the most support, and decides to make grants to certain projects.

Fig. 5 .

 5 Fig. 5. The knowledge model using individual and group entities of information

Acknowledgement. The development of the knowledge management system is partially funded by Technology Strategy Board (TSB) (KTP009100-508802) and supported by University of Reading.