ARTICLE IN PRESS H.-F. Wang, C-T. Wu/ Computers 8 Operations Research i(Im)Il-l tep 1 (1)Construct user-groups, U j=1,2,.5.(UUI=182 (2270.8=182) CECF-NC Recall CF Recall item-groups, Pl, i=1,2. Step 2. Compute relative purchase priority w 0 Step 3. Compute similarity measures between user-groups by Common item set function i. e Eq (6)and Non-Common item 0 set function i.e. Eq. (8). 00.10.20.40.50.60.8090.95 Step 4. Derive out-of-clique probability measures by Eq (4)as shown in Table 4. Note that the probability measures in each ow are normalized and ensured that they sum up to 1 PCECF-NC Precision CF Precision Step 1. Set up parameters on in-clique effects(0)and profit consideration(B), respectively. For implementation, the system could set up e and B as arbitrary values. In the 00.10.20.40.50.60.809095 experiments, we set up 0 to be 0, 0. 1. 0.2,..., 1 and B to be 0. 0.2.0.4.... 1 for testing. Step 2. The users are tested as new users or historical users by setting 0=0 or 0< 0s 1, respectively Satisfaction levels(b) -rCECF-NC FI -CF FI are also defined to be 0.7, 0.8, 0.9 for experiments. Budget limits(B)are set arbitrary values that are lower than the 0.4 ummation of all items' price Step 3. Classify target user into one user-group by (up(og)U FJ=1,2,,5 0 Step 3. 1. The situation is simulated in a manner where some 00.10.20.40.50.60.809095 historical users are recommended when we set 0<0<1 tep 3. 2. The situation is simulated wherein some new users Fig. 4. Comparison of CECF-NC and CI (ul. u2,.. )are recommended by CECF-NC or CECF-C espectively when we set 0=0, which is shown in Tables 5 recall, precision, and F1 under sample values 0, with a neighborhood size of 20. Note that when 0=1, CECF-C and CECF- the probability measures for him/ her could be only derived NC both become the CF since out-of-clique effects no longer exist. from out-of-clique measures For instance, in Table 4, the In Table 7, the results of an average performance show that CECF-C probability of U to P is 0.025, which shall be the same with and CECF-NC perform better than CF except 0=0. In addition,it that of ui to p5 and p]6 in Table 5. The value is 0.0008 could be also observed that CECF-Nc performs slightly better than instead of 0.025 due to normalization CECF-C. In Fig. 4, CECF-NC has been compared with CF: in the Step 4 and Step 5 figure, the CECF-NC performs much better than CF in recall and F1 (p-value <0.001, 95% confidence level), and slightly better in In the two steps, the target user's metadata is obtained and fed recision to the analytical model, and the output of recommendations is then yielded. We skip the list of the recommendation results and Experiment 2. In Experiment 1, the average performance is directly compare the performance of the proposed operation better and more stable when CECF-NC and 6=0.6 are used. module by the following experiments. Therefore, we set up 0 to be 0.6 and continue experimenting on the analytical model by introducing B to be 0, 0. 2, 0. 4,..., 1 and Experiment 1. The performance of the recommendation results satisfaction level(b/)to be 0.7, 0.8, 0.9 under users' budget limits. n CECF-C, CECF-NC, and CF is shown in Table 7, with evaluation of We compare the CECF-NC with profit consideration as well non-profit consideration in terms of recall, precision and Fl as shown in Fig. 5; and the difference of profit gained in the two Table 7 cases are presented in Fig. 6. In Fig. 5. the results show that even Average performance of CECF-C, CECF-NC and CF. when profit consideration is introduced, the recommendation performance would not be poorer(p-value <0.05. 95% confidence level). In Fig. 6, the results show that profit increases along B Recall Precision F1 Recall Precision F1 0.928 .93908670925 Experiment 3. In this experiment, we compare three recommen- 96309030942 0.959 dation schemes of CF, CECF-NC with profit consideration (B=0.2) 0.945 .968091 and CECF-NC with non-profit consideration in terms of their F1 0.945 0.968 0.907 0.945 0.967 measures. Fig. 7 shows that the F1 values increase as the neighborhood size increases from 3. 5. 7, 10, to 20. In addition, 09100.945 Fig. 7 showed consistent results we obtained from the previous 0945 1(C0.457 0930 0.569 two experiments, that is, the CECF-Nc with profit/non-profit consideration outperforms conventional CF. 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 Step 1. (1) Construct user-groups, Uj , j¼1, 2, y, 5, j[jUj j ¼ 182. (227*0.8ffi182) (2) Construct item-groups, Pi , i¼1, 2, y, 16, j[jPi j ¼ 192 Step 2. Compute relative purchase priority wj i . Step 3. Compute similarity measures between user-groups by Common item set function i.e. Eq. (6) and Non-Common item set function i.e. Eq. (8). Step 4. Derive out-of-clique probability measures by Eq. (4) as shown in Table 4. Note that the probability measures in each row are normalized and ensured that they sum up to 1. Online operation procedures (testing data) Step 1. Set up parameters on in-clique effects (y) and profit consideration (b), respectively. For implementation, the system could set up y and b as arbitrary values. In the experiments, we set up y to be 0, 0.1, 0.2,y,1 and b to be 0, 0.2, 0.4,y,1 for testing. Step 2. The users are tested as new users or historical users by setting y¼0 or 0oyr1, respectively. Satisfaction levels ðbfj Þ are also defined to be 0.7, 0.8, 0.9 for experiments. Budget limits ðBfj Þ are set arbitrary values that are lower than the summation of all items’ prices. Step 3. Classify target user into one user-group by Uj ¼ fufjðogÞjfj ¼ 1j ,2j , ... ,Fj ,j ¼ 1,2, ... ,5g. Step 3.1. The situation is simulated in a manner where some historical users are recommended when we set 0oyr1. Step 3.2. The situation is simulated wherein some new users ðu1 1,u1 2, ...Þ are recommended by CECF-NC or CECF-C respectively when we set y¼0, which is shown in Tables 5 and 6. Note that when a target user is regarded as a new user, the probability measures for him/her could be only derived from out-of-clique measures. For instance, in Table 4, the probability of U1 to P16 is 0.025, which shall be the same with that of u1 1 to p16 5 and p16 6 in Table 5. The value is 0.0008 instead of 0.025 due to normalization. Step 4 and Step 5. In the two steps, the target user’s metadata is obtained and fed to the analytical model, and the output of recommendations is then yielded. We skip the list of the recommendation results and directly compare the performance of the proposed operation module by the following experiments. Experiment 1. The performance of the recommendation results on CECF-C, CECF-NC, and CF is shown in Table 7, with evaluation of recall, precision, and F1 under sample values y, with a neighborhood size of 20. Note that when y¼1, CECF-C and CECF￾NC both become the CF since out-of-clique effects no longer exist. In Table 7, the results of an average performance show that CECF-C and CECF-NC perform better than CF except y¼0. In addition, it could be also observed that CECF-NC performs slightly better than CECF-C. In Fig. 4, CECF-NC has been compared with CF; in the figure, the CECF-NC performs much better than CF in recall and F1 (p-valueo0.001, 95% confidence level), and slightly better in precision. Experiment 2. In Experiment 1, the average performance is better and more stable when CECF-NC and y¼0.6 are used. Therefore, we set up y to be 0.6 and continue experimenting on the analytical model by introducing b to be 0, 0.2, 0.4,y,1 and satisfaction level ðbfj Þ to be 0.7, 0.8, 0.9 under users’ budget limits. We compare the CECF-NC with profit consideration as well as non-profit consideration in terms of recall, precision and F1 as shown in Fig. 5; and the difference of profit gained in the two cases are presented in Fig. 6. In Fig. 5, the results show that even when profit consideration is introduced, the recommendation performance would not be poorer (p-valueo0.05, 95% confidence level). In Fig. 6, the results show that profit increases along b increases from 0 to 1. Experiment 3. In this experiment, we compare three recommen￾dation schemes of CF, CECF-NC with profit consideration (b¼0.2) and CECF-NC with non-profit consideration in terms of their F1 measures. Fig. 7 shows that the F1 values increase as the neighborhood size increases from 3, 5, 7, 10, to 20. In addition, Fig. 7 showed consistent results we obtained from the previous two experiments, that is, the CECF-NC with profit/non-profit consideration outperforms conventional CF. Table 7 Average performance of CECF-C, CECF-NC and CF. y CECF-NC CECF-C Recall Precision F1 Recall Precision F1 0 0.297 0.458 0.325 0.297 0.458 0.325 0.1 0.877 0.928 0.939 0.867 0.925 0.932 0.2 0.900 0.942 0.962 0.893 0.938 0.955 0.4 0.908 0.943 0.963 0.903 0.942 0.959 0.5 0.910 0.945 0.968 0.910 0.945 0.968 0.6 0.911 0.945 0.968 0.907 0.945 0.967 0.7 0.910 0.945 0.968 0.910 0.945 0.968 0.8 0.910 0.945 0.968 0.910 0.945 0.968 0.9 0.910 0.945 0.968 0.910 0.945 0.968 1(CF) 0.457 0.930 0.569 0.457 0.930 0.569 0 0.2 0.4 0.6 0.8 1 0 CECF-NC_Recall CF_Recall 0 0.2 0.4 0.6 0.8 1 CECF-NC_Precision CF_Precision 0 0.2 0.4 0.6 0.8 1 CECF-NC_F1 CF_F1 0.1 0.2 0.4 0.5 0.6 0.8 0.9 0.95 0 0.1 0.2 0.4 0.5 0.6 0.8 0.9 0.95 0 0.1 0.2 0.4 0.5 0.6 0.8 0.9 0.95 Fig. 4. Comparison of CECF-NC and CF. 10 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