Y Jiang et al. / Decision Support Systems 48(2010)470-479 Table 8 Table 11 A potential customer. Customers whose ratings are easily predicted Customer Expertise CPU Battery Audio eo Customer Age Expertise CPU Battery Audio Video P A G 0 0080.140.70008 weights in Table 7 to predict the ratings of customer c with the char- 005073001021 acteristics given in Table 8. The matching classification experts, RA. R, Rg, and Rio, are found first. Among them, Ra is not applied because clas- sification expert, Ro is more specific than RA Table 12 Thereafter, we combine the multiple evidence bodies given by the Customers with inconclusive ratings. three classification experts to calculate the aggregate multi-class clas- Customer Age Expertise CPU Battery Audio Video P Ge sification information. Since the weights for E7, Eg, and E1o are 61 AG0030380270.32 d.12 00026041033 respectively. The evidence bodies used to classify customer c together C46s Y A 0000390.160.45 with their weights are presented in Table 9. Following the method in Cso GA0.250470280.00 Section 3. 4, we are able to derive the aggregate multi-class classifica- tion results as shown in table 10 We thus can predict the rating of customer c. Furthermore, the Table 13 reason why the product is or is not recommended to customer c is Comparison of classification accuracy lucid. If the product is recommended to customer c, he/she will rate Method C4.5 SVM CSMC the product as'Poor'with 26% probability and'Average'with% prob- Accuracy 0.706 0.755 0.686 Proposed model 0.794 0.824 ability. The chance he/she will consider the product to be"Good is only 10%. The product thus should not be recommended to the C4.5: The decision customer SVM: Support vector machine algorithm For the 102 customers in the test data set two kinds of classifi- CBA: Classification based on associations algorithm cation results are obtained by the rating classifier. The first kind of CSMC: Combination strategy for multi-class classification. results assigns most of the probabilities to one rating, which make estimating customer response easy. For example, in Table 11 the pre- tion, integrates conflicting form classification experts, and dicted ratings for the four customers are 'Good, 'Good, 'Good, and eventually builds the rating ation model. This explains why Average with probabilities of 94%, 79%, 70%, and 73%, respectively. the proposed method can nore accuracy than conventional The decisions are easy to make that is, recommend the product to methods. customers Ca12, C435, and Ca78, but not to cso- However, indistinct ratings may also take place, resulting in a less 5 Conclusions and future work valuable recommendation. For example, the ratings in Table 12 do not provide definite recommendations. Under such circumstances, addi- The recommendation system is an important tool to offer per tional measures may be taken to ensure customer satisfaction For sonalized service and maximize customer satisfaction. Current liter example,additional information may be elicited to obtain more accu- ature regards a recommendation system as a success if a potential Warranty may also be offered to increase the odds of satisfaction. We argue that a truly successful recommendation system should be ate needs information and preferences data: a greater discount or a customer takes the advice and purchases the recommended product. Table 13 provides accuracy comparisons among different methods the one that maximizes the customers' after-sale satisfaction, not one The experiments of the decision tree method (C4.5)and support vector that just lures customers into the act of purchasing. We emphasize machine(SVM) algorithm were carried out using the Weka software that a good recommendation system not only considers what the system, which is an open source tool for machine learning [ 39). The customer needs, but also ensures customers contentment. The main classification based on associations(CBA)algorithm was studied using contributions of this research are twofold. First, we make a distinction the software developed by the authors in [26]. and the combination between the customer purchase and the customer endorsement. strategy for multi-class classification(CSMC) was implemented by When a customer follows advice to purchase a product(DO), it does software system MATLAB 7.0. not imply that the person is truly pleased(FEEL)with the decision he Due to the existence of conflicting ratings, it is hard for traditional she made. Second, to maximize a customers satisfaction level, we methods to mine useful multi-class pattern propose a more effective and efficient rating classification model based classifiers. On the contrary, the proposed I with the on the customer's profile and feedback. The associative classification uncertain environment elegantly. It retains nforma- method proposed in this research is capable of mining multi-class information from the need-rating data. It predicts the appeal of the specific product to the customer through integrated utilization of he set of evidence bodies that matches the target customer. formation. and the recommendation is meticulous and valuable Despite the contribution of this research, there are limitations, and idence body further works can be done. The first important work is to investigate he factors that impact a customer's feelings Many attributes such as Eg the demographic and psychological characteristics, purchase and con- sumption environment, and customers' expectation, may well have significant influence on customers' feelings toward a specific product. Therefore, it is crucial to indentify the factors important for modeling cation results of the potential customer. rating classification, so as to predict the customer's satisfaction leve effectively Another work is to elicit customers'needs and preferences. The rating classification aims to recommend the right products based onweights in Table 7 to predict the ratings of customer c with the char￾acteristics given in Table 8. The matching classification experts, R4, R7, R9, and R10, are found first. Among them, R4 is not applied because clas￾sification expert, R9 is more specific than R4. Thereafter, we combine the multiple evidence bodies given by the three classification experts to calculate the aggregate multi-class clas￾sification information. Since the weights for E7, E9, and E10 are 61, 91, and 20, respectively, which are normalized to .35, .53, and .12 respectively. The evidence bodies used to classify customer c together with their weights are presented in Table 9. Following the method in Section 3.4, we are able to derive the aggregate multi-class classifica￾tion results as shown in Table 10. We thus can predict the rating of customer c. Furthermore, the reason why the product is or is not recommended to customer c is lucid. If the product is recommended to customer c, he/she will rate the product as ‘Poor’ with 26% probability and ‘Average’ with 53% prob￾ability. The chance he/she will consider the product to be “Good” is only 10%. The product thus should not be recommended to the customer. For the 102 customers in the test data set, two kinds of classifi- cation results are obtained by the rating classifier. The first kind of results assigns most of the probabilities to one rating, which makes estimating customer response easy. For example, in Table 11 the pre￾dicted ratings for the four customers are ‘Good’, ‘Good’, ‘Good’, and ‘Average’ with probabilities of 94%, 79%, 70%, and 73%, respectively. The decisions are easy to make, that is, recommend the product to customers c412, c435, and c478, but not to c501. However, indistinct ratings may also take place, resulting in a less valuable recommendation. For example, the ratings in Table 12 do not provide definite recommendations. Under such circumstances, addi￾tional measures may be taken to ensure customer satisfaction. For example, additional information may be elicited to obtain more accu￾rate needs information and preferences data; a greater discount or a warranty may also be offered to increase the odds of satisfaction. Table 13 provides accuracy comparisons among different methods. The experiments of the decision tree method (C4.5) and support vector machine (SVM) algorithm were carried out using the Weka software system, which is an open source tool for machine learning [39]. The classification based on associations (CBA) algorithm was studied using the software developed by the authors in [26], and the combination strategy for multi-class classification (CSMC) was implemented by software system MATLAB 7.0. Due to the existence of conflicting ratings, it is hard for traditional methods to mine useful multi-class patterns and construct accurate classifiers. On the contrary, the proposed method can deal with the uncertain environment elegantly. It retains useful conflicting informa￾tion, integrates conflicting rules to form classification experts, and eventually builds the rating classification model. This explains why the proposed method can attain more accuracy than conventional methods. 5. Conclusions and future work The recommendation system is an important tool to offer per￾sonalized service and maximize customer satisfaction. Current liter￾ature regards a recommendation system as a success if a potential customer takes the advice and purchases the recommended product. We argue that a truly successful recommendation system should be the one that maximizes the customers' after-sale satisfaction, not one that just lures customers into the act of purchasing. We emphasize that a good recommendation system not only considers what the customer needs, but also ensures customer's contentment. The main contributions of this research are twofold. First, we make a distinction between the customer purchase and the customer endorsement. When a customer follows advice to purchase a product (DO), it does not imply that the person is truly pleased (FEEL) with the decision he/ she made. Second, to maximize a customer's satisfaction level, we propose a more effective and efficient rating classification model based on the customer's profile and feedback. The associative classification method proposed in this research is capable of mining multi-class information from the need-rating data. It predicts the appeal of the specific product to the customer through integrated utilization of in￾formation, and the recommendation is meticulous and valuable. Despite the contribution of this research, there are limitations, and further works can be done. The first important work is to investigate the factors that impact a customer's feelings. Many attributes such as the demographic and psychological characteristics, purchase and con￾sumption environment, and customers' expectation, may well have significant influence on customers' feelings toward a specific product. Therefore, it is crucial to indentify the factors important for modeling rating classification, so as to predict the customer's satisfaction level effectively. Another work is to elicit customers' needs and preferences. The rating classification aims to recommend the right products based on Table 8 A potential customer. Customer Age Expertise CPU Battery Audio Video c MG A G A G Table 9 The set of evidence bodies that matches the target customer. Evidence body PAG Θ Weight E7 0.28 0.48 0.24 0 0.35 E9 0.31 0.54 0 0.15 0.53 E10 0 0.37 0.44 0.19 0.12 Table 10 Rating classification results of the potential customer c. Poor Average Good Θ 0.26 0.53 0.10 0.11 Table 11 Customers whose ratings are easily predicted. Customer Age Expertise CPU Battery Audio Video P A G Θ c412 YG AG A A 0.02 0.04 0.94 0.00 c435 YG GG A A 0.01 0.12 0.79 0.08 c478 OA G A A G 0.08 0.14 0.70 0.08 c501 YA AA G G 0.05 0.73 0.01 0.21 Table 12 Customers with inconclusive ratings. Customer Age Expertise CPU Battery Audio Video P A G Θ c417 MG G A A G 0.03 0.38 0.27 0.32 c452 YA GG A A 0.00 0.26 0.41 0.33 c469 YA GG A G 0.00 0.39 0.16 0.45 c504 MG A G G A 0.25 0.47 0.28 0.00 Table 13 Comparison of classification accuracy. Method C4.5 SVM CBA CSMC Proposed model Accuracy 0.706 0.755 0.686 0.794 0.824 Nomenclature C4.5: The decision tree method SVM: Support vector machine algorithm CBA: Classification based on associations algorithm CSMC: Combination strategy for multi-class classification. Y. Jiang et al. / Decision Support Systems 48 (2010) 470–479 477