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 sub￾modules. 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