ARTICLE IN PRESS H.-F. Wang C-T. Wu/Computers 8 Operations Research I(Im)I 3.3.3.2. Online operation procedures 4.1. A case study with experiments Step 1. Set up parameters on in-clique effects(0)and profit In the experiments, 227 customers are divided randomly into 1.1. Maximal profit strategy, set B=1: 20%/80% as testing and training data in an echo. We would 1. 2. Win-win strat conduct three experiments with different goals. In the first 1.3. Best service strategy, set B=0 experiment, we shall compare the recommendation performance Step 2. On-line inquiry of target users'profiles of demographic of conventional CF with our proposed recommendation approach features(up(og)), binary basket data( Curt. Pa ) the desired CECF in two cases of CECF-C and CECF-NC and the three schemes satisfaction level(b), and the budget limit(Br') are all with a fixed neighborhood size of 20. In the second Step 3. Classify target user into proper user-group by experiment, we would compare the recommendation perfor mance as well as the profit gained with respect to suppliers U={up(og)=1,2 market strategies as: (1)B=l yields maximal profit strategy and 3.1. A historical user with basket data(0<0s 1)compute (2)Be(0, 1) yields the win-win strategy, (3)B=0 emphasizes the urchase probabilities on each item with CECF-C(Eq(6)) customers benefit of the best service strategy. In the third CECF-NC(Eq (8)). experiment, we compare the sensitive Fl values with respect to 3. 2. A new user without basket data(0=O)retrieve out-of- the neighborhood sizes(3, 5. 7, 10, and 20)under three schemes clique probability measures as purchase probability on each of CF, CECF-NC with profit consideration (B=0. 2)and CECF-Nc with non-profit consideration. Step 4. Derive metadata from purchase probabilities(Eq (5) Three measures of recall, precision and F1 equest as input to Step 5. evaluation. Different values of parameters chosen to Step 5. Run the analytical model and yield recommendation demonstrate their impacts as sensitivity analysis. We pick one of the echoes for illustration in the following section. All experimental procedures would be shown in compliance with the procedures proposed in Section 3.3.3 (Table 4-6). 4. Case study: laptops Rs of a 3C retailer Offline operation procedures(training data) 3C industries of Taiwan have the most advanced technologies in the world. Among various electronic products, the experiments ur proposed rs are conducted specifically with la because of three reasons.( 1) Laptop transactions are usually Purchase probabilities of new users by cECF-NC(0=0) fewer than those of other electronic products so introducing an RS would be meaningful to attract the users; (2)fewer transactions re difficult to exploit when introducing the rS, so our pro 0007000070000080.0008 RS aims to solve this situation by incorporating clique effects; and pops are all highly priced so that the profit consi 0000 4h Following the provided data of a 3C retailer. the prototype of us 0.0028 0.0028 0.0028 0.00840.00840.00060.0006 would be system was established and evaluated in this section by first escribing the given database: the laptop data set contains 915 market baskets including 227 customers and 192 items. The rpes of items in the basket are ranged from two to eight for each Table 6 er. The users information is revealed by user types(define Purchase probabilities of new users by CECF-C(0=0). h users' demographic features by the 3C retailer)and five user- groups are yielded (U, U. U. U. UP). The item attributes(k)are PnB四 denoted as: (1)central processing unit( CPU). (2)random-access 0.0025 32 exclusive groups. Due to incomplete data, there are only oe oe an 0.000 00240.0024 0.0005 Table 4 Out-of-clique probability measures. p U20003008200050222000500600365000100540029002900540026000700341 U20.00 0.244 0.0870.00527800210.0620.32600 002200220072001 0.2700.018 0.024 0060022 U500000.08800060264001600580.33100010055001700200070002200060029 P P3 P p Tot 0003 0.2190.0050.0600.36 00290.0550025000600351 0.254 0.347 U000 0.2390.012 90.2330.0080.0570.3600.0010059002200230067002600040.0361 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 3.3.3.2. Online operation procedures. Step 1. Set up parameters on in-clique effects (y) and profit consideration (b) if adopt 1.1. Maximal profit strategy, set b¼1; 1.2. Win–win strategy, set a bAð0,1Þ; 1.3. Best service strategy, set b¼0. Step 2. On-line inquiry of target users’ profiles of demographic features ðufjðogÞÞ, binary basket data ðCuf t ,pdi Þ; the desired satisfaction level ðbfj Þ, and the budget limit ðBfj Þ. Step 3. Classify target user into proper user-group by Uj ¼ fufjðogÞjfj ¼ 1j ,2j , ... ,Fj , j ¼ 1,2, ... ,Jg. 3.1. A historical user with basket data (0oyr1)—compute purchase probabilities on each item with CECF-C (Eq. (6)) or CECF-NC (Eq. (8)). 3.2. A new user without basket data (y¼0)—retrieve out-of￾clique probability measures as purchase probability on each item. Step 4. Derive metadata from purchase probabilities (Eq. (5)) and user’s request as input to Step 5. Step 5. Run the analytical model and yield recommendation list. 4. Case study: laptops RS of a 3C retailer 3C industries of Taiwan have the most advanced technologies in the world. Among various electronic products, the experiments of our proposed RS are conducted specifically with laptops because of three reasons. (1) Laptop transactions are usually fewer than those of other electronic products so introducing an RS would be meaningful to attract the users; (2) fewer transactions are difficult to exploit when introducing the RS, so our proposed RS aims to solve this situation by incorporating clique effects; and (3) laptops are all highly priced so that the profit consideration would be more applicable. Following the provided data of a 3C retailer, the prototype of the system was established and evaluated in this section by first describing the given database; the laptop data set contains 915 market baskets including 227 customers and 192 items. The types of items in the basket are ranged from two to eight for each user. The user’s information is revealed by user types (defined with users’ demographic features by the 3C retailer) and five user￾groups are yielded (U1 , U2 , U3 , U4 , U5 ). The item attributes (k) are denoted as: (1) central processing unit (CPU), (2) random-access memory (RAM), (3) brand, (4) storage capacity, and (5) weight. By our classification rules with K¼5, the item-groups consist of 32 exclusive groups. Due to incomplete data, there are only 16 non-empty item-groups. 4.1. A case study with experiments In the experiments, 227 customers are divided randomly into 20%/80% as testing and training data in an echo. We would conduct three experiments with different goals. In the first experiment, we shall compare the recommendation performance of conventional CF with our proposed recommendation approach CECF in two cases of CECF-C and CECF-NC, and the three schemes are all with a fixed neighborhood size of 20. In the second experiment, we would compare the recommendation perfor￾mance as well as the profit gained with respect to supplier’s market strategies as: (1) b¼1 yields maximal profit strategy and (2) bAð0,1Þ yields the win–win strategy, (3) b¼0 emphasizes the customer’s benefit of the best service strategy. In the third experiment, we compare the sensitive F1 values with respect to the neighborhood sizes (3, 5, 7, 10, and 20) under three schemes of CF, CECF-NC with profit consideration (b¼0.2) and CECF-NC with non-profit consideration. Three measures of recall, precision and F1 will be used for evaluation. Different values of parameters were chosen to demonstrate their impacts as sensitivity analysis. We pick one of the echoes for illustration in the following section. All experimental procedures would be shown in compliance with the procedures proposed in Section 3.3.3 (Table 4–6). Offline operation procedures (training data) Table 4 Out-of-clique probability measures. NC P1 P2 P3 P4 P5 P6 P7 P8 P9 P11 P12 P13 P14 P15 P16 Total U1 0.003 0.082 0.005 0.222 0.005 0.060 0.365 0.001 0.054 0.029 0.029 0.054 0.026 0.007 0.034 1 U2 0.003 0.085 0.004 0.244 0.019 0.053 0.335 0.000 0.060 0.024 0.020 0.072 0.023 0.006 0.030 1 U3 0.003 0.087 0.005 0.278 0.021 0.062 0.326 0.001 0.057 0.022 0.022 0.072 0.012 0.000 0.021 1 U4 0.004 0.095 0.001 0.270 0.018 0.062 0.323 0.001 0.050 0.024 0.024 0.063 0.016 0.006 0.022 1 U5 0.000 0.088 0.006 0.264 0.016 0.058 0.331 0.001 0.055 0.017 0.020 0.070 0.022 0.006 0.029 1 C P1 P2 P3 P4 P5 P6 P7 P8 P9 P11 P12 P13 P14 P15 P16 Total U1 0.003 0.081 0.005 0.219 0.005 0.060 0.366 0.001 0.055 0.029 0.029 0.055 0.025 0.006 0.035 1 U2 0.004 0.077 0.005 0.216 0.013 0.054 0.359 0.000 0.063 0.031 0.025 0.065 0.025 0.005 0.034 1 U3 0.004 0.081 0.006 0.254 0.015 0.062 0.347 0.001 0.058 0.027 0.026 0.066 0.015 0.000 0.025 1 U4 0.007 0.088 0.001 0.239 0.012 0.063 0.349 0.001 0.053 0.034 0.031 0.054 0.016 0.004 0.024 1 U5 0.000 0.077 0.009 0.233 0.008 0.057 0.360 0.001 0.059 0.022 0.023 0.067 0.026 0.004 0.036 1 Table 5 Purchase probabilities of new users by CECF-NC (y¼0). p2 1 p2 2 p2 13 p4 2 p4 3 p16 5 p16 6 u1 1 0.0026 0.0026 0.0026 0.0070 0.0070 0.0008 0.0008 u2 1 0.0027 0.0027 0.0027 0.0078 0.0078 0.0007 0.0007 u3 1 0.0028 0.0028 0.0028 0.0088 0.0088 0.0004 0.0004 u4 1 0.0031 0.0031 0.0031 0.0087 0.0087 0.0007 0.0007 u5 1 0.0028 0.0028 0.0028 0.0084 0.0084 0.0006 0.0006 Table 6 Purchase probabilities of new users by CECF-C (y¼0). p2 1 p2 2 p2 13 p4 2 p4 3 p16 5 p16 6 u1 1 0.0025 0.0025 0.0025 0.0069 0.0069 0.0008 0.0008 u2 1 0.0024 0.0024 0.0024 0.0068 0.0068 0.0007 0.0007 u3 1 0.0025 0.0025 0.0025 0.0079 0.0079 0.0004 0.0004 u4 1 0.0028 0.0028 0.0028 0.0076 0.0076 0.0008 0.0008 u5 1 0.0024 0.0024 0.0024 0.0073 0.0073 0.0005 0.0005 H.-F. Wang, C.-T. Wu / Computers & Operations Research ] (]]]]) ]]]–]]] 9 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