Decision Support Systems 48(2010)470-479 Contents lists available at science Direct Decision Support Systems ELSEVIER journalhomepagewww.elsevier.com/locate/dss Maximizing customer satisfaction through an online recommendation system: a novel associative classification model Yuanchun Jiang a b, * Jennifer Shang Yezheng Liua. c School of management, Hefei University of Technology. Hefei, Anhui 230009, China b The Joseph M. Katz Graduate School of Business, University of Pittsburgh, Pittsburgh,PA 15260,USA Key Laboratory of process Optimization and Intelligent Decision Making, Ministry of Education, Hefei, Anhui 230009, China ARTICLE O A BSTRACT Available online 17 June 2009 Offering online personalized recommendation services helps improve customer satisfaction. Conventionally a recommendation system is considered as a success if clients purchase the recommended products. However, the act of purchasing itself does not guarantee satisfaction and a truly successful recommendation system hould be one that maximizes the customer's after-use gratification. By employing an innovative associative classification method, we are able to predict a customers ultimate pleasure. Based on customers character- ion Rating tics, a product will be recommended to the potential buyer if our model predicts his/her satisfaction level will be high. The feasibility of the proposed recommendation system is validated through laptop Inspiron 1525. o 2009 Elsevier B V. All rights reserved. 1 Introduction recommendation system as successful if customers end up purchasing the suggested product(s). However, buying a product does not neces- important factors that impact a customers product selection and satis- scenario bel e client is pleased wi Personalization of product information has become one of the most sarily imply the client is pleased with the product. Let,'s consider a faction in todays competitive and challenging market. Personalized James is in need of a laptop computer. He visits online stores to look service requires firms to understand customers and offer goods or services for information and compare prices and performance of various lap- that meet their needs. Successful firms are those that provide the right tops. Between the two laptop series, Inspiron 1525 and Aspire 5735, products to the right customers at the right time and for the right price. James is uncertain which one would best fit his needs. He decides to As a type of information technology aimed to support personalized turn to the recommendation system for help. After gaining knowledge service, recommendation systems are widely used by e-commerce of James's needs and personal profile, the system recommends the actitioners and have become an important research topic in infor- Inspiron 1525. Once James follows the advice and makes his purchase, mation sciences and decision support systems [ 25]. Recommendation the recommendation system deems that it did a great job because systems are decision aids that analyze customers prioronline behavior James bought the laptop it recommended. However, after I weeks use and present information on products to match customers preferences. of the laptop, James writes a review as follows: ".a good product, but Through analyzing the patrons purchase history or communicating with not the one I really want, "It turns out James is not content with the them, recommendation systems employ quantitative and qualitative recommendation. This exemplifies the case that a customer may have methods to discover the products that best suit the customer. Most of purchased the recommended product(s), but the recommendation the current recommendation systems recommend products that have system was not successful in pleasing the customer-its ultimate goal. a high probability of being purchased [3]. They employ content-based It is therefore clear that a customers acceptance of a recommendation filtering(CBF)[41 ] collaborative filtering (CF)[18. and other data min- is not equivalent to its success. A recommendation system must endure ing techniques, for example, decision tree [12, association rule 38. the test of time. Only when customers claim that the products are what and semantic approach[25. Other literature focuses on the influence they like after their practical usage can one claim that the system has of recommendation systems on customer's purchase behavior 3.32]. made effective recommendations. This requires not only matching They argue that the recommendation decision should be based not on customers'needs, but also satisfying customers'wants. In other words purchase probability, but rather on the sensitivity of purchase proba- the recommendation system should only recommend a product if its bility due to the recommendation action. Common wisdom regard satisfaction rating is predicted to be high. How can a customers satisfaction of a specific product be measured Corresponding author. School of Management, Hefei University of Technology, and attained? The rapid development of e-commerce affords us an opportunity to predict customers' reactions after they use a product. E-mailaddress:yuanchunjiang@gmail.com(YJiang Many online stores, such as Amazon. com and DelLcom encourage 0167-9236/S- see front matter o 2009 Elsevier B V. All rights reserved oi:10.1016/ds200906.006Maximizing customer satisfaction through an online recommendation system: A novel associative classification model Yuanchun Jiang a,b, ⁎, Jennifer Shang b , Yezheng Liu a,c a School of Management, Hefei University of Technology, Hefei, Anhui 230009, China b The Joseph M. Katz Graduate School of Business, University of Pittsburgh, Pittsburgh, PA 15260, USA c Key Laboratory of Process Optimization and Intelligent Decision Making, Ministry of Education, Hefei, Anhui 230009, China article info abstract Available online 17 June 2009 Keywords: Online recommendation Customer satisfaction Associative classification Rating classification Offering online personalized recommendation services helps improve customer satisfaction. Conventionally, a recommendation system is considered as a success if clients purchase the recommended products. However, the act of purchasing itself does not guarantee satisfaction and a truly successful recommendation system should be one that maximizes the customer's after-use gratification. By employing an innovative associative classification method, we are able to predict a customer's ultimate pleasure. Based on customer's character￾istics, a product will be recommended to the potential buyer if our model predicts his/her satisfaction level will be high. The feasibility of the proposed recommendation system is validated through laptop Inspiron 1525. © 2009 Elsevier B.V. All rights reserved. 1. Introduction Personalization of product information has become one of the most important factors that impact a customer's product selection and satis￾faction in today's competitive and challenging market. Personalized service requires firms to understand customers and offer goods or services that meet their needs. Successful firms are those that provide the right products to the right customers at the right time and for the right price. As a type of information technology aimed to support personalized service, recommendation systems are widely used by e-commerce practitioners and have become an important research topic in infor￾mation sciences and decision support systems [25]. Recommendation systems are decision aids that analyze customer's prior online behavior and present information on products to match customer's preferences. Through analyzing the patron's purchase history or communicating with them, recommendation systems employ quantitative and qualitative methods to discover the products that best suit the customer. Most of the current recommendation systems recommend products that have a high probability of being purchased [3]. They employ content-based filtering (CBF) [41], collaborative filtering (CF) [18], and other datamin￾ing techniques, for example, decision tree [12], association rule [38], and semantic approach [25]. Other literature focuses on the influence of recommendation systems on customer's purchase behavior [3,32]. They argue that the recommendation decision should be based not on purchase probability, but rather on the sensitivity of purchase proba￾bility due to the recommendation action. Common wisdom regards a recommendation system as successful if customers end up purchasing the suggested product(s). However, buying a product does not neces￾sarily imply the client is pleased with the product. Let's consider a scenario below. James is in need of a laptop computer. He visits online stores to look for information and compare prices and performance of various lap￾tops. Between the two laptop series, Inspiron 1525 and Aspire 5735, James is uncertain which one would best fit his needs. He decides to turn to the recommendation system for help. After gaining knowledge of James's needs and personal profile, the system recommends the Inspiron 1525. Once James follows the advice and makes his purchase, the recommendation system deems that it did a great job because James bought the laptop it recommended. However, after 1week's use of the laptop, James writes a review as follows: “…a good product, but not the one I really want.” It turns out James is not content with the recommendation. This exemplifies the case that a customer may have purchased the recommended product(s), but the recommendation system was not successful in pleasing the customer—its ultimate goal. It is therefore clear that a customer's acceptance of a recommendation is not equivalent to its success. A recommendation system must endure the test of time. Only when customers claim that the products are what they like after their practical usage can one claim that the system has made effective recommendations. This requires not only matching customers' needs, but also satisfying customers' wants. In other words, the recommendation system should only recommend a product if its satisfaction rating is predicted to be high. How can a customer's satisfaction of a specific product be measured and attained? The rapid development of e-commerce affords us an opportunity to predict customers' reactions after they use a product. Many online stores, such as Amazon.com and Dell.com encourage Decision Support Systems 48 (2010) 470–479 ⁎ Corresponding author. School of Management, Hefei University of Technology, Hefei, Anhui 230009, China. E-mail address: yuanchunjiang@gmail.com (Y. Jiang). 0167-9236/$ – see front matter © 2009 Elsevier B.V. All rights reserved. doi:10.1016/j.dss.2009.06.006 Contents lists available at ScienceDirect Decision Support Systems j o u r n a l h om e p a g e : www. e l s ev i e r. c om / l o c a t e / d s s