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. 2010.03.011ARTICLE 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