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. 2010.03.011ARTICLE 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 win￾win 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 recommenda￾tion 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 strategy￾oriented 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 well￾arranged. 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 recom￾mendation 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