Y Jiang et al. / Decision Support Systems 48(2010)470-479 The proposed model Which laptop will please the Which laptop the customer may customer most? be interested in buying Note Good Fig 1. Differences between the existing recommendation systems and the proposed model customers to write online reviews on their websites; information from 2. 1. Recommendation systems these reviews is then often used to support a firms product strategy and customer relationship management [ 11, 13 In the online reviews. Since the development of the first recommendation system by customers can discuss their needs, preferences, personal profile, and Goldberg and colleagues [17 various recommendation systems and voice their opinions about a product as poor, average, or good. From such related technologies such as CBF and CF[18, 41] have been reported. need-rating data, it is easy to obtain personalized information and Among them, the user-based collaborative filtering (CF)[23is customers'after-usesatisfactionleveloftheproductUsingpersonalsuccessfullyadoptedbyAmazoncomandDell.com.Itfindsasimilar information and responses, the online store can more accurately predict user group for the target buyer and recommends products that have customers' true sentiments toward a specific product, and recommend a been rated by users in the reference group but not yet viewed by the more suitable product for the potential customer to enjoy. target buyer. However, the user-based CF has some limitations. One is This research proposes a rating classification model to estimate a its difficulty in measuring the similarities between users, and the potential customer's satisfaction level. It builds a rating classifier for a other is the scalability issue. As the number of customers and products product by discovering rules from the need-rating database collected increases, the computation time of algorithms grows exponentially for the product. The rules imply the Co-relationship between cus- [21. The item-based CF[16 was proposed to overcome the scalability tomers' needs, preferences, demographic profile, and their ratings for problem as it calculates item similarities in an offline basis. It assumes sifier will predict his/her response toward the recommended product related to the items that he/she has already purchased Gre similar or the product. For a new customer with specific characteristics, the clas- that a user will be more likely to purchase items that and categorize it into certain class labels, such as poor, average and good. The predicted ratings estimate the customer's satisfaction level for the product. Differences between the existing recommendation Table 1 systems and the proposed one are illustrated in Fig. 1. Summary of research methods on recommendation system and associative classification. This research proposes a novel associative classification model, which (a) Motivation and objectives of warious recommendation systems first mines multi-class classification information from need-rating data, Literature Motivation then constructs a rating classifier, and finally predicts customers' ratings (2, 10, 16, 17,20,, 23,411 Which products meet the Recommend product for products. We organize the rest of the paper as follows In Section 2 we customer's preferences best? vith high probability review the literature of recommendation systems and associative 3.32 What is the influence of Recommend produ classification models. Section 3 proposes the innovative methodology to which are receptive to ustomer's purchase behavior? the recommendation. ddress the rating classification problem. A case study used to illustrate the This paper Which products can achieve a effectiveness of the proposed model is given in Section 4. Section 3 high after-use satisfaction level? with high after-use comprises the Summary, conclusions, and future research. satisfaction leveL (b) Comparing associative classification models 2. Literature review class rules lation system and associative classification. A summary of relatin- [26,31,36.371 The literature review focuses on two perspectives: the recomm research methods are given in Table 1 and explained in detail below. and this paper√customers to write online reviews on their websites; information from these reviews is then often used to support a firm's product strategy and customer relationship management [11,13]. In the online reviews, customers can discuss their needs, preferences, personal profile, and voice their opinions about a product as poor, average, or good. From such need-rating data, it is easy to obtain personalized information and customers' after-use satisfaction level of the product. Using personal information and responses, the online store can more accurately predict customers' true sentiments toward a specific product, and recommend a more suitable product for the potential customer to enjoy. This research proposes a rating classification model to estimate a potential customer's satisfaction level. It builds a rating classifier for a product by discovering rules from the need-rating database collected for the product. The rules imply the co-relationship between cus￾tomers' needs, preferences, demographic profile, and their ratings for the product. For a new customer with specific characteristics, the clas￾sifier will predict his/her response toward the recommended product and categorize it into certain class labels, such as poor, average and good. The predicted ratings estimate the customer's satisfaction level for the product. Differences between the existing recommendation systems and the proposed one are illustrated in Fig. 1. This research proposes a novel associative classification model, which first mines multi-class classification information from need-rating data, then constructs a rating classifier, and finally predicts customers' ratings for products. We organize the rest of the paper as follows. In Section 2 we review the literature of recommendation systems and associative classification models. Section 3 proposes the innovative methodology to address the rating classification problem. A case study used toillustrate the effectiveness of the proposed model is given in Section 4. Section 3 comprises the Summary, conclusions, and future research. 2. Literature review The literature review focuses on two perspectives: the recommen￾dation system and associative classification. A summary of relative research methods are given in Table 1 and explained in detail below. 2.1. Recommendation systems Since the development of the first recommendation system by Goldberg and colleagues [17], various recommendation systems and related technologies such as CBF and CF [18,41] have been reported. Among them, the user-based collaborative filtering (CF) [23] is successfully adopted by Amazon.com and Dell.com. It finds a similar user group for the target buyer and recommends products that have been rated by users in the reference group but not yet viewed by the target buyer. However, the user-based CF has some limitations. One is its difficulty in measuring the similarities between users, and the other is the scalability issue. As the number of customers and products increases, the computation time of algorithms grows exponentially [21]. The item-based CF [16] was proposed to overcome the scalability problem as it calculates item similarities in an offline basis. It assumes that a user will be more likely to purchase items that are similar or related to the items that he/she has already purchased. Fig. 1. Differences between the existing recommendation systems and the proposed model. Table 1 Summary of research methods on recommendation system and associative classification. (a) Motivation and objectives of various recommendation systems Literature Motivation Objective [2,10,16,17,20,21,23,41] Which products meet the customer's preferences best? Recommend products with high probability. [3,32] What is the influence of recommendation systems on customer's purchase behavior? Recommend products which are receptive to the recommendation. This paper Which products can achieve a high after-use satisfaction level? Recommend products with high after-use satisfaction level. (b) Comparing associative classification models Literature Mine multi￾class rules Classify using multiple rules Provide classification reasons [26,31,36,37] √ [24] √ [27] and this paper √√ √ Y. Jiang et al. / Decision Support Systems 48 (2010) 470–479 471