ARTICLE IN PRESS Computers Operations Research I(aIm)Ill-Ill Contents lists available at Science Direct Computers Operations research ELSEVIER journalhomepagewww.elsevier.com/locate/caor A strategy-oriented operation module for recommender systems In e-commerce Hsiao-Fan Wang, Cheng-Ting Wu Department of Industrial Engineering and Engineering Management, National Tsing Hua University, No. 101, Section 2 Kuang Fu Road, Hsinchu, Taiwan 30013, ROC ARTICLE INFO ABSTRACT Electronic commerce(EC)has become an important support for business and is regarded as an efficient Keywords: ystem that connects suppliers with online users. Among the applications of EC, a recommender system (RS)is undoubtedly a popular issue to make the best recommendation to the users. Even if many approaches have been proposed to perfect the recommendation, a comprehensive module comprising of essential sub-modules of input profiles, a recommendation scheme, and an output interface of Clique-effects collaborative filtering recommendations in the Rs is still lacking. Besides, the fundamental issue of profit consideration for an C company is not stressed in general terms. Therefore, this study aims struct an rs with a strategy-oriented operation module regarding the above aspects; and with this module, an approach named clique-effects collaborative filtering(CECF) for predicting the consumers purchase behavior was proposed. Finally, we applied our proposed module to a 3C retailer in Taiwan, and promising results Scope and Purpose: This study aims to construct a comprehensive module for the recommender recommender system. By utilizing the proposed module with marketing strategies and an effective on-line interface scheme, the recommender system could emphasize not only the customer satisfaction as conventional recommender system suggested, but also the suppliers profit which shall be an important issue to an E-commerce company. Thus, a better recommendation environment could e 2010 Elsevier Ltd. All rights reserved. 1. Introduction system is urgent and essential for an EC company. By providing more helpful information to users, faster and more satisfactory Electronic commerce(EC) has been widely used by online decisions can be made: and thus, opportunities of retaining users to perform different daily activities through the Internet. customers and gaining profits are higher Online shopping is one of the popular applications among these Many EC suppliers use the recommender systems(RSs)to activities. Instead of conventional shopping. EC provides alter- out the preferences of target users so that the right products can native ways for users to get information on products such as price, be suggested [45 ] A well-established RS can add value to an EC availability, suppliers, substitutes, and even manufacturing company in several ways-(1) users can retrieve product process [39, 54]. For competitiveness, Ec companies need to information easily, (2) cross-selling for users can be enhanced, develop higher business interoperability on their electronic and (3)users' loyalty can be sustained by good service. There are market places by improving the electronic market functions numerous studies in the fields of social networks [34] and [52, 53]. The enhancement of electronic market functions could information filtering techniques [42]. In social networks, people lead to an overall reduction of interaction cost for business with similar characteristics tend to associate with each other The interoperation on all types of electronic market places [15]. use of social network structure generally allows the ec to identify among the numerous EC functions which provide so the products of likely interest to the target users based on some vailable information, it is difficult for online users to information provided by the members of the network [ 19, 28.On ick and effective decisions [48]. Facing fierce market the other hand, information filtering techniques that analyze competition and impatient users, a personalized decision support users' preferences and help EC Web sites achieve accurate product selection By filtering the information provided by the users, the techniques aim to track the purchase behavior of users Corresponding author. TeL: +88635742654x42654: fax: +88635722685 and recommend proper products. Among information filtering techniques, collaborative filtering(CF[25. 45 46 is one of the er e 2010 Elsevier Ltd. All rights reserved. doi:10.1016jc Please cite this article as: Wang H-F, Wu C-T. A strategy-oriented operation module for recommender systems in E-commerce. Computers and Operations Research(2010). doi: 10. 1016 j cor. 2010.03.011
A strategy-oriented operation module for recommender systems in E-commerce Hsiao-Fan Wang , Cheng-Ting Wu Department of Industrial Engineering and Engineering Management, National Tsing Hua University, No. 101, Section 2 Kuang Fu Road, Hsinchu, Taiwan 30013, ROC article info Keywords: Electronic commerce Recommender system Marketing strategy Clique-effects collaborative filtering abstract Electronic commerce (EC) has become an important support for business and is regarded as an efficient system that connects suppliers with online users. Among the applications of EC, a recommender system (RS) is undoubtedly a popular issue to make the best recommendation to the users. Even if many approaches have been proposed to perfect the recommendation, a comprehensive module comprising of essential sub-modules of input profiles, a recommendation scheme, and an output interface of recommendations in the RS is still lacking. Besides, the fundamental issue of profit consideration for an EC company is not stressed in general terms. Therefore, this study aims to construct an RS with a strategy-oriented operation module regarding the above aspects; and with this module, an approach named clique-effects collaborative filtering (CECF) for predicting the consumer’s purchase behavior was proposed. Finally, we applied our proposed module to a 3C retailer in Taiwan, and promising results were obtained. Scope and Purpose: This study aims to construct a comprehensive module for the recommender systems. The proposed strategy-oriented operation module comprises the essential parts of a recommender system. By utilizing the proposed module with marketing strategies and an effective on-line interface scheme, the recommender system could emphasize not only the customer’s satisfaction as conventional recommender system suggested, but also the supplier’s profit which shall be an important issue to an E-commerce company. Thus, a better recommendation environment could be displayed. & 2010 Elsevier Ltd. All rights reserved. 1. Introduction Electronic commerce (EC) has been widely used by online users to perform different daily activities through the Internet. Online shopping is one of the popular applications among these activities. Instead of conventional shopping, EC provides alternative ways for users to get information on products such as price, availability, suppliers, substitutes, and even manufacturing process [39,54]. For competitiveness, EC companies need to develop higher business interoperability on their electronic market places by improving the electronic market functions [52,53]. The enhancement of electronic market functions could lead to an overall reduction of interaction cost for business interoperation on all types of electronic market places [15]. However, among the numerous EC functions which provide so much available information, it is difficult for online users to make quick and effective decisions [48]. Facing fierce market competition and impatient users, a personalized decision support system is urgent and essential for an EC company. By providing more helpful information to users, faster and more satisfactory decisions can be made; and thus, opportunities of retaining customers and gaining profits are higher. Many EC suppliers use the recommender systems (RSs) to find out the preferences of target users so that the right products can be suggested [45]. A well-established RS can add value to an EC company in several ways—(1) users can retrieve product information easily, (2) cross-selling for users can be enhanced, and (3) users’ loyalty can be sustained by good service. There are numerous studies in the fields of social networks [34] and information filtering techniques [42]. In social networks, people with similar characteristics tend to associate with each other. The use of social network structure generally allows the EC to identify the products of likely interest to the target users based on some information provided by the members of the network [19,28]. On the other hand, information filtering techniques that analyze users’ preferences and help EC Web sites achieve accurate product selection. By filtering the information provided by the users, the techniques aim to track the purchase behavior of users and recommend proper products. Among information filtering techniques, collaborative filtering (CF) [25,45,46] is one of the ARTICLE IN PRESS Contents lists available at ScienceDirect journal homepage: www.elsevier.com/locate/caor Computers & Operations Research 0305-0548/$ - see front matter & 2010 Elsevier Ltd. All rights reserved. doi:10.1016/j.cor.2010.03.011 Corresponding author. Tel.: +886 3 5742654x42654; fax: +886 3 5722685. E-mail address: hfwang@ie.nthu.edu.tw (H.-F. Wang). Please cite this article as: Wang H-F, Wu C-T. A strategy-oriented operation module for recommender systems in E-commerce. Computers and Operations Research (2010), doi:10.1016/j.cor.2010.03.011 Computers & Operations Research ] (]]]]) ]]]–]]]
ARTICLE IN PRESS most commonly adopted method. The concept n be applied at different levels of he community's opinions as input ng information that can development o in Section 3. T case study in Section 5, with sug 2. Literature re systems Schafer et al. [45.46 investigated the ir suD-module recommendations, and (3) re interface between the tw the current deve 2.1. Input source market baskets; le for recommender systems in E-commerce
ARTICLE IN PRESS most commonly adopted method. The concept of the CF is much related to the social network. The CF technique uses collaborative information from ‘‘neighbors,’’ which are defined as users with similar behavior to the target user. CF is also regarded as the most effective method for the RS. However, CF’s drawback is that no recommendation could be made if a user’s related data are sparse [26]. On the other hand, excessive emphasis on recommendation performance could lead to the neglect of the profit, which is also an essential concern for an EC company. Aside from this, although there are different approaches to retrieve the needed information for recommendation, a systematic and comprehensive decision module is still lacking. Therefore, the time spent on data retrieval can be long, and the recommended products may not match the users’ desires. In particular, without a structural module, documenting the recommending procedure becomes difficult, and achieving the goal of ‘‘the right goods for the right person’’ becomes impossible. With these concerns, we aim to propose a strategy-oriented operation module that could be comprehensively applied to EC Web sites as a decision support mechanism so that the choice of various marketing strategies that consider profit for both suppliers and users can be developed. In addition, under the framework of the proposed recommender module, we also propose a clique-effects collaborative filtering (CECF) technique to predict users’ purchase behavior. In particular, this paper presents the modeling perspective to the e-service system i.e. the recommender system. The proposed RS module aims to fulfill the profits of the customers and suppliers; the final stage of product selection is described as a linear bi-objective model, of which all required arguments are derived from the offline database and the CECF. The paper is organized as follows. Section 2 discusses the literature related to the framework, issues, and the further development of an RS. The strategy-oriented operation module applied to an RS will be developed along with the proposed CECF in Section 3. Then we apply our proposed RS to a 3C retailer as a case study in Section 4. Finally, concluding remarks are given in Section 5, with suggestions on further research. 2. Literature review of the infrastructure of recommender systems Schafer et al. [45,46] and Montaner et al. [37] have investigated the infrastructure of an RS in the framework of three sub-modules: (1) input sources of the users’ profiles, (2) output of recommendations, and (3) recommendation methods as the interface between the two. In this section, we shall briefly review the current developments with respect to these three submodules. 2.1. Input sources Usually, input sources include users’ individual profiles which could be used to gather preferences for specific items, item attributes, ratings, and keywords or even purchase history [46]. Schafer et al. have classified input sources into two types [46]: (1) single users’ profiles—the preferences of the target user for whom we are recommending, and (2) communities’ opinions as an input regarding the general community of other users, that is, the target user is represented by the community. The two types of inputs allow the RS to make suggestions for different reasons. For a target user, the individual profiles are inputted to the recommender agent to provide personalized information, whereas the input profiles of the community are fed into the RS to reflect opinions from multiple individuals as a whole. Therefore, these two types can be applied at different levels of personalization. In particular, the community’s opinions as input are helpful in reinforcing or complementing information that can be retrieved from single user’s profiles. This could be specified by the well-known issue of the ‘‘new user’’ problem, which is one of the cases in the ‘‘ramp-up’’ problem [27]. Recommendation for new users faces the challenge that the neighbors are hard to identify in a start-up company since the new users’ profiles are lacking. When this phenomenon is translated into a user–item relation matrix, the matrix will be sparse. In particular, if a highly dimensional database is developed for an RS, the problem of identifying neighborhood becomes severe from the sparse user– item relation matrix. In order to solve the problems of sparse data or missing values, many approaches based on CF have been proposed. The issues of sparse matrix or missing values are often tackled with dimensionality reduction techniques [7,14,24,43]. Several dimensionality reduction techniques have been developed and applied to Jester, Movielens and EachMovie datasets. And in Eigentaste, Goldberg et al. [14] divided the recommendation process into two stages: online and offline operations. In the offline stage, the authors exploited the principal component analysis (PCA) to facilitate dimensionality reduction so that user’s profiles which are formed through rating the gauge set are projected into an eigen plane. Consequently, in the online stage, the target user is asked to rate the gauge set to receive recommendations. An alternative approach to estimate the missing values and to reduce the dimensionality of user–item relation matrix is the method of singular value decomposition (SVD), which has been exploited by Sarwar et al. [43]. SVD appears to be a common method for matrix factorization that results in the best lower rank approximations of the user–item relation matrix; however, Sarwar et al. suggested that the SVD-based method would yield better results in dense datasets of which a start-up company does not possess. Kim and Yum [24] further suggested an evolved PCA-iterative method, in which SVD is performed iteratively to improve the accuracy of imputed values based on prior results. Nevertheless, to accommodate the dimensionality reduction to the recommendation process, the new user usually requires to rate on the specifically designated item set, for example, the gauge set, which could contain items that the new user never knows; besides, the size of designated item set should also be carefully controlled in case of driving the impatient customers out of the system. As indicated by Herlocker et al. [18] and Linden et al. [31], using PCA- or SVD-based techniques for dimensionality reduction would cause a lower recommendation quality since recommendations for items are more restricted to specific subjects; examining a small user sample such as the gauge set, the chosen neighborhoods are less similar with the target user. Moreover, Bell et al. [5] argued that the methods using imputed ratings, which significantly outnumber the original ratings, rely on imputation risk; and such risk would distort the data due to inaccurate imputation. To realize a user’s purchase behavior, the information revealed by a user’s profiles is often investigated. Generally, there are two kinds of user’s profiles that are commonly searched and collected. These are the user’s ratings [45] and market basket data [35]. User’s ratings refer to the scores given to item attributes by a user, and the user’s ratings are often analyzed to define preference. On the other hand, market basket data contain a user’s purchase history and probably demographic features. Specifically, each item presented in a user’s basket data could either be ‘‘0’’ or ‘‘1’’ to denote whether an item is purchased , ‘‘1’’, or not, ‘‘0’’. There are always a number of transactional data in the market baskets; hence, management of these input profiles should be easier to maintain and retrieve. 2 H.-F. Wang, C.-T. Wu / Computers & Operations Research ] (]]]]) ]]]–]]] Please cite this article as: Wang H-F, Wu C-T. A strategy-oriented operation module for recommender systems in E-commerce. Computers and Operations Research (2010), doi:10.1016/j.cor.2010.03.011
ARTICLE IN PRESS H.-F. Wang. C.-T. Wu Computers Ope The usual techniques used to maintain user's profiles are prevent poor prediction due to rarely relevant information [44]. history-based model [37] and the vector space m because the conventional CF approach utilizes A history-based model lists purchase rec bors to make a prediction for a target user, of non-neighbors out of considera- the impacts of ng the effects could be also space mo for large am adopted in t 2.2. Output of In g information simplest item. A s consider it or unord advertising st tion, displ cross-selling recommenda are not rel promotions. I products that 2.3. Recon Recommer and efficiency items accc know us use a rela mapping purchased lles fore, of ed by es involved: the le 1, we list of EC constructed ded as built Shih developed Please cite this article as -commerce. Computers and Opera
ARTICLE IN PRESS The usual techniques used to maintain user’s profiles are the history-based model [37] and the vector space model [11,40]. A history-based model lists purchase records, navigation history, or the contents of e-mail boxes to define users’ profiles. In the vector space model, items are represented with a vector of features or attributes, usually words or concepts (such as a binary column to denote the purchased state or a column to denote the attributive value of an item), with an associated value. The vector space model is more efficient for computation, so it is often used for large amounts of data. For this reason, it is also the model adopted in this paper to maintain the database. 2.2. Output of recommendations In general, the output is a suggestion of product(s) containing information on item type, quantity, and appearance [46]. The simplest form of a suggestion is the recommendation of a single item. A single item increases the chance that a user will seriously consider it desirable. More commonly, an RS provide an ordered or unordered recommendation list for a user [38]. Some advertising strategies can also be embedded in the recommendation, displaying bundled items, which could help enhance cross-selling and up-selling. By comparing bundled items with a recommendation list, bundled items may include products that are not related to the users since they are generated for promotions. In contrast, a recommendation list shows a set of products that satisfies users’ preferences to a certain degree. 2.3. Recommendation methods Recommendation methods are concerned with the accuracy and efficiency of prediction and presentation of the recommended items according to users’ input sources. For an RS, it is critical to know users’ preferences systematically. An essential concept is to use a relational database which is constructed offline. Then by mapping a new user to the database, a product that has been purchased by the same type of historical users can easily be picked up for the target user [29]. Clustering analysis is the technique that groups users/items with similar characteristics/properties into one group. By clustering, the search dimensionality can be reduced which speeds up the mapping process. A wide range of applications have been implemented by clustering techniques, and one of these is used to predict unknown users based on the group they belong to [49]. By analyzing the properties of the groups, we can learn about the characteristics of new users by identifying the group they belong to and thus provide them with the items that the same group has mostly bought. Besides, clustering analysis is also a very useful tool for looking for the ‘‘neighbors’’ in the information filtering technology. That is, the users called the neighbors are chosen by certain methods, such as clustering techniques, to support the prediction [6]. Information filtering technology has the ability to define user preferences with little effort. It is divided into two main categories [26]—collaborative filtering (CF) and content-based filtering (CBF). CF is the most popular approach to predict the probability that a user will purchase a specific item based on other users’ preferences [21]. A CF method functions by matching people with similar interests and then making recommendations. However, in the initial state of an RS, the main problem would be insufficient users’ profiles sustain the prediction basis while using CF. Consequently, the drawback of CF is the requirement of some relevant rating data given by the target user. Usually, by clustering users into groups before predicting, group influences could be utilized by recommendation methods on the target user to prevent poor prediction due to rarely relevant information [44]. Furthermore, because the conventional CF approach utilizes preferences of neighbors to make a prediction for a target user, it leaves additional influences of non-neighbors out of consideration. As a result, research tends to discriminate the impacts of neighbors from non-neighbors [23]; by integrating the effects caused by the two sources, better performance could be also expected. CBF is the technology of analysis based on terms in the content such as texts or documents on the Web site. It considers term frequency in the content and its relation to the user’s preference. However, with other media such as music or movies, its performance is not as good as text content because these objects are not easily indexed. In addition, the maintenance of numerous heterogeneous electronic product catalogues on the Internet is still a tough task [16]. Nevertheless, CF is still most commonly used since it is flexible and easily adaptable to an EC’s RS [7]. Therefore, in this paper, we would incorporate the concept of CF into our system as the basic recommendation mechanism. In addition to CF and CBF, another technique requires the private information of a user. Demographic filtering (DF) explains users by their personal demographics [17]. A DF approach uses descriptions of people to learn the probability that an item is most preferred by what type of persons. Therefore, this method would lead to the same recommendation if the users have similar personal data. However, the DF approach requires more information regarding a user’s privacy; therefore, DF is confronted with the problem that it is not easy to collect users’ demographic descriptions. Consequently, the DF method requires collaborating with other methods such as CF or CBF [37]. Besides the aforementioned filtering techniques, rules derived from the market basket analysis between items in large databases also account for an RS. Market basket analysis has been a popular system in finding the correlation among baskets [2,41]. One of the techniques is the famous association rules method, which was first introduced by Agrawal et al. [3]. Association rules have been used to find the pattern of the probability of buying a specific product when another product is purchased. In such a recommending environment, many rules have been developed on how the different purchase behaviors of users can be treated [20]. Therefore, Sarwar et al. also proposed a method of associationrule based recommendation (ABR) in 2000 [42]. However, for the huge amount of transaction data, there may be many biased rules that would affect the precision of the recommendation. Therefore, the market basket analysis shall be conducted with the aid of filtering techniques such as CF, and the common concept of the CF method adapted to the binary market basket data as proposed by Mild and Reutterer [36]. 2.4. Roles with their goals in a recommender system In the current RS, there are three common roles involved: the supplier, the system developer, and the user. In Table 1, we list possible considerations for constructing an RS. In the fields of EC trading, Li and Wang proposed a multi-agent-based model with a win-win negotiation approach of which the agents seek to strike a fair deal that also maximizes the payoff for everyone involved [30]. However, such kind of win-win negotiation mechanism has not been discussed in the RSs with more comprehensive scale. For the existing research, the ‘‘performance of recommendation’’ is an attribute that benefits users. Therefore, when ‘‘more is better’’ is stressed, only the number of sold products is maximized but not necessarily the profit. In other words, an RS is usually constructed from a user’s standpoint. Only a few RSs could be regarded as built from a supplier’s perspective. For instance, Liu and Shih developed H.-F. Wang, C.-T. Wu / Computers & Operations Research ] (]]]]) ]]]–]]] 3 Please cite this article as: Wang H-F, Wu C-T. A strategy-oriented operation module for recommender systems in E-commerce. Computers and Operations Research (2010), doi:10.1016/j.cor.2010.03.011
ARTICLE IN PRESS H -F Wang, C-T. Wu/Computers 8 Operations Research i(am)Il-l Table Roles and resolution in recommender systems. User Supplier System developer Objective Constraints qu&Cs) Problem types zation problem Maximization problem Multi-objective problem Maximization problem Note: (u): user:(s): supplier. o fulfill the demands of oneself, o(s)objective of the supplier: maximize profit or products sold. C(u constraint of the user: budgets in hand and as) constraint of the supplier: fulfill demands of users. a weighted RFM-based method for an RS 32,33. where RFM With the above concerns, in this study, we propose a strategy- means recency, frequency, and monetary: it considers the user's oriented operation module for the rs comprising(1)an offline lifetime value which is helpful in extending market share in the database, (2)CECF, and (3)the analytical model. An offline long run. However, for an RS constructed from the viewpoint of database that could be mathematically supported for the rs is system developer, issues should be considered that not only to developed. The database consists of three parts-user-group data fulfill the user's needs(preferences, budgets)but also to raise the item-group data, and the relations in between. The offline supplier's profit. Changchien et al. discussed sales promotion database is designed with the two characteristics: (1)the users based on businesses'marketing strategies, pricing strategies, and and the items are classified into groups according to their respective features/attributes(see Sections 3. 1.1 and 3. 1.2).As win situation[9]. However, the study prioritized the probability of suggested in the literatures, PCA- or SVD-based approaches may an inequitable supplier so that it may be difficult to keep a users lose prediction accuracy due to excessively restricted dataset loyalty. Therefore, it is also necessary to construct an RS that from which the neighborhood is formed. Thus we adopt the allows both parties to justify their priorities. classification technique for dimensionality reduction. We regard any individual in a group as an information provider, which is 2.5. Summary and discussion especially important to a start-up RS with rare data, (2)the group effects are much easier to be retrieved By bringing out additional From the brief review of the recent RSs, some aspects could be effects from the groups of users and items, we aim to dilute the there is no complete manipulated module that supports all inconsistent imputed data like average scores sub-modules of input module, output module, and recommenda- tion interface an RS. The researchers also realized that through over prediction, the priority of group effects shall be well quantitative measurement, the performance of the system can be error e, under the proposed offline database, we general applications in an RS From the viewpoint of managing an group's effects, CECF is likely helpful in solving the situation of EC site and its RS, it is more robust and convenient if an analytical sparse data and the so-called"ramp up"problem. In addition,we model comprising the three sub-modules can be imported also introduce an analytical model proposed by wang and wu facilitate the product selection process. With this regard, [51]. The analytical model could allow the system developer to developing a comprehensive module that can achieve the actively adjust the priority between the supplier's profit and the transparent requirements of the decision-support process and user's satisfaction level. Therefore, in the next section, we shall provide a good solution for recommendation purposes is propose the strategy-oriented operation module whose cores necessary and would be presented in this study consist of ceCf and the cal model: the module aims to Second, we found merits and deficiencies in each of the describe the recommendat ocess and provide better recom- existing recommendation approaches. Since RSs have different mendation performance types of input sources such as users ratings or market basket lata, the corresponding recommendation method will be a key ub-module that determines the success of an RS. as the 3. the proposed recommendation module applications in CE, personal profiles of target users are first used to match their neighbors: the purchase behaviors of the Based on the issues specified in Section 2. 4 that an RS shall neighbors are then exploited to predict target users' choices. provide three roles to be switched and the summary in Section However, for an EC Web site that is a start-up or is selling 2.5, we propose an RS( Fig. 1)with the recommendation module products with high prices, it would be confronted with the composed of three sub-modules-input, the recommendation problem that not enough basket data support the market basket method, and output. The input sub-module deals with the input recommendation performance would be very poor. Since the new system would be the demographic information, the binary basket ser with few personalized information is difficult to categorize data, and the target user's requests of the desired satisfaction the communitys opinions could be adopted to complement the level and budget limit. The output sub-module would provide the insufficient information. For a user whose personal profiles are recommended items from the result of the recommendation identity 2n. the community s opinions reinforce tne users online o perations. Thnenrecompmedation method which is the core Please cite this article as: Wang H-F, Wu C-T. A strategy-oriented operation module for recommender systems in E-com Computers and Operations Research(2010). doi: 10. 1016/j. cor. 2010.03.011
ARTICLE IN PRESS a weighted RFM-based method for an RS [32,33], where RFM means recency, frequency, and monetary; it considers the user’s lifetime value which is helpful in extending market share in the long run. However, for an RS constructed from the viewpoint of system developer, issues should be considered that not only to fulfill the user’s needs (preferences, budgets) but also to raise the supplier’s profit. Changchien et al. discussed sales promotion based on businesses’ marketing strategies, pricing strategies, and users’ purchasing behavior, which could potentially be a winwin situation [9]. However, the study prioritized the probability of an inequitable supplier so that it may be difficult to keep a user’s loyalty. Therefore, it is also necessary to construct an RS that allows both parties to justify their priorities. 2.5. Summary and discussion From the brief review of the recent RSs, some aspects could be emphasized to improve an RS. First, it should be noted that so far, there is no complete manipulated module that supports all sub-modules of input module, output module, and recommendation interface in an RS. The researchers also realized that through quantitative measurement, the performance of the system can be better controlled and evaluated. This triggers our main goal in this study to develop an operation module for systematic analysis and general applications in an RS. From the viewpoint of managing an EC site and its RS, it is more robust and convenient if an analytical model comprising the three sub-modules can be imported to facilitate the product selection process. With this regard, developing a comprehensive module that can achieve the transparent requirements of the decision-support process and provide a good solution for recommendation purposes is necessary and would be presented in this study. Second, we found merits and deficiencies in each of the existing recommendation approaches. Since RSs have different types of input sources such as user’s ratings or market basket data, the corresponding recommendation method will be a key sub-module that determines the success of an RS. As the applications in CF, personal profiles of target users are first used to match their neighbors’; the purchase behaviors of the neighbors are then exploited to predict target users’ choices. However, for an EC Web site that is a start-up or is selling products with high prices, it would be confronted with the problem that not enough basket data support the market basket analysis (dataset is sparse or with missing values); therefore, that recommendation performance would be very poor. Since the new user with few personalized information is difficult to categorize, the community’s opinions could be adopted to complement the insufficient information. For a user whose personal profiles are already known, the community’s opinions reinforce the user’s identity [23]. With the above concerns, in this study, we propose a strategyoriented operation module for the RS comprising (1) an offline database, (2) CECF, and (3) the analytical model. An offline database that could be mathematically supported for the RS is developed. The database consists of three parts—user-group data, item-group data, and the relations in between. The offline database is designed with the two characteristics: (1) the users and the items are classified into groups according to their respective features/attributes (see Sections 3.1.1 and 3.1.2). As suggested in the literatures, PCA- or SVD-based approaches may lose prediction accuracy due to excessively restricted dataset from which the neighborhood is formed. Thus we adopt the classification technique for dimensionality reduction. We regard any individual in a group as an information provider, which is especially important to a start-up RS with rare data, (2) the group effects are much easier to be retrieved. By bringing out additional effects from the groups of users and items, we aim to dilute the imprecise prediction caused by rare data, and to prevent inconsistent imputed data like average scores. However, to avoid the imputed group effects predominating over prediction, the priority of group effects shall be wellarranged. Therefore, under the proposed offline database, we base on CF to propose a clique-effects approach, namely, CECF. With the scheme of adjustable weights between individual’s and group’s effects, CECF is likely helpful in solving the situation of sparse data and the so-called ‘‘ramp up’’ problem. In addition, we also introduce an analytical model proposed by Wang and Wu [51]. The analytical model could allow the system developer to actively adjust the priority between the supplier’s profit and the user’s satisfaction level. Therefore, in the next section, we shall propose the strategy-oriented operation module whose cores consist of CECF and the analytical model; the module aims to describe the recommendation process and provide better recommendation performance for the RS. 3. The proposed recommendation module Based on the issues specified in Section 2.4 that an RS shall provide three roles to be switched and the summary in Section 2.5, we propose an RS (Fig. 1) with the recommendation module composed of three sub-modules—input, the recommendation method, and output. The input sub-module deals with the input profiles of a target user; the types of profiles considered in the system would be the demographic information, the binary basket data, and the target user’s requests of the desired satisfaction level and budget limit. The output sub-module would provide the recommended items from the result of the recommendation method. Both input and output sub-modules are categorized into online operations. The recommendation method, which is the core Table 1 Roles and resolution in recommender systems. User Supplier System developer Objective O(u) O(s) Win–win strategy Maximal profit strategy O(u) & O(s) O(s) Constraints C(u) C(s) C(u) & C(s) C(u) & C(s) Problem types Maximization problem Maximization problem Multi-objective problem Maximization problem Note: (u): user; (s): supplier.O(u) Objective of the user: fulfill the demands of oneself, O(s) objective of the supplier: maximize profit or products sold, C(u) constraint of the user: budgets in hand and C(s) constraint of the supplier: fulfill demands of users. 4 H.-F. Wang, C.-T. Wu / Computers & Operations Research ] (]]]]) ]]]–]]] Please cite this article as: Wang H-F, Wu C-T. A strategy-oriented operation module for recommender systems in E-commerce. Computers and Operations Research (2010), doi:10.1016/j.cor.2010.03.011
ARTICLE IN PRESS H.-F. Wang C-T. Wu / Computers 8 Operations Research I (m)I Target user browses in Identify target user'sprofil Retrieve relational data user'srequests Metadata of the user a retrieved off-line database Analytica decal model database Online Operations Fig. 1. The proposed recommendation module. of the recommending module, functions with an online analytical Table 2 model under the offline database constructed from three Classification rules when K=3 group end item-group end, and the relations in between. Exploiting the proposed CECF approach, the offline Attribute labels database provides required information retrieval of the target 1 user's purchase probability measure on each item. The analytical model is then run by metadata composed of the target user's request and what has been retrieved from the offline database. In articular, the analytical model uses a bi-objective function that [a1. aaMa would allow choice between the win-win strategy and th 2,xx} maximal profit strategy, which were proposed by Wang and wu 51. The win-win strategy not only matches the users taste but also enhances the suppliers profit, whereas the maximal profit trategy recommends products based on maximization of profit. This section is organized as follows. First, we would specify the construction of the offline database including the user-group a Zk,...,], to be an attribute vector of pa, then the set of items Item-group data. Then the proposed clique-effects approach in the database is P=lPa(ax)ld=1, 2,. D). All items in the ased on CF(CECF) would be presented in Section 3. 2. Finally database are further classified into mutually exclusive ve would clarify online and offline operations as well as present item-groups as P=lPa(ax)d=1, 2,. D, i=1, 2,. I). each the analytical model in Section 3.3 with IPiI=D, and thus U Pi=P and E,Di=D In particular. 3.1. Offline operations we classify the items with respect to the item attributes. A threshold of each attribute value is given; each item with specific attribute values above those thresholds will be assigned to In this section, we would specify the construction of the offline the corresponding group. The number of attributes (K) would be database including the user-groups data and item-groups data referred with its power set and then 2 item-groups are generated. For instance, in Table 2, the number of item-groups 3.1.1. Item-groups with their properties generated is 8 when K is 3: an item would be distributed into Let d be the items in the market basket, with each item Class 5 only if its attribute values in al. 2 are higher than the denoted as Pd, where d=1,., D. Define Yp=[1,2 thresholds of a1, a 2 as well as its a3 value lower than the Please cite this article as: Wang H-F, Wu C-T. A strategy-oriented operation module for recommender systems in E-commerce. Computers and Operations Research(2010). doi: 10. 1016 j cor. 2010.03.011
ARTICLE IN PRESS of the recommending module, functions with an online analytical model under the offline database constructed from three parts—user-group end, item-group end, and the relations in between. Exploiting the proposed CECF approach, the offline database provides required information retrieval of the target user’s purchase probability measure on each item. The analytical model is then run by metadata composed of the target user’s request and what has been retrieved from the offline database. In particular, the analytical model uses a bi-objective function that would allow choice between the win–win strategy and the maximal profit strategy, which were proposed by Wang and Wu [51]. The win–win strategy not only matches the user’s taste but also enhances the supplier’s profit, whereas the maximal profit strategy recommends products based on maximization of profit. This section is organized as follows. First, we would specify the construction of the offline database including the user-group and item-group data. Then the proposed clique-effects approach based on CF (CECF) would be presented in Section 3.2. Finally, we would clarify online and offline operations as well as present the analytical model in Section 3.3. 3.1. Offline operations In this section, we would specify the construction of the offline database including the user-groups data and item-groups data. 3.1.1. Item-groups with their properties Let D be the items in the market basket, with each item denoted as pd, where d¼1,y,D. Define Cpd ¼ ½a1,a2, ... , ak, ... ,aK pd to be an attribute vector of pd, then the set of items in the database is P ¼ fpdðakÞjd ¼ 1,2, ... ,Dg. All items in the database are further classified into mutually exclusive item-groups as Pi ¼ fpdiðakÞjdi ¼ 1i ,2i , ... ,Di ,i ¼ 1,2, ... ,Ig, each with jPi j ¼ Di , and thus SI i ¼ 1 Pi ¼ P and PI i ¼ 1 Di ¼ D. In particular, we classify the items with respect to the item attributes. A threshold of each attribute value is given; each item with specific attribute values above those thresholds will be assigned to the corresponding group. The number of attributes (K) would be referred with its power set and then 2K item-groups are generated. For instance, in Table 2, the number of item-groups generated is 8 when K is 3; an item would be distributed into Class 5 only if its attribute values in a1, a2 are higher than the thresholds of a1, a2 as well as its a3 value lower than the Target user browses in Identify target user’sprofiles satisfied? N Update periodically Online Operations Off-line Operations interface Y Modify target user’sprofiles Analytical model Data retrieved Retrieve relational data New basket database Metadata of the user off-line database The recommendation list user’srequests Fig. 1. The proposed recommendation module. Table 2 Classification rules when K¼3. Class Attribute labels 1 Non 2 fa1g\fa2,a3g 3 fa2g\fa1,a3g 4 fa3g\fa1,a2g 5 fa1,a2g\fa3g 6 fa1,a3g\fa2g 7 fa2,a3g\fa1g 8 H.-F. Wang, C.-T. Wu / Computers & Operations Research ] (]]]]) ]]]–]]] 5 Please cite this article as: Wang H-F, Wu C-T. A strategy-oriented operation module for recommender systems in E-commerce. Computers and Operations Research (2010), doi:10.1016/j.cor.2010.03.011