Predicting customer purchase behavior in the e-commerce context

Predicting customer purchase behavior is an interesting and challenging task. In the e-commerce context, meeting this challenge requires confronting many problems not observed in the traditional business context. Recommender system technology has been widely adopted by e-commerce websites. However, a traditional recommendation algorithm cannot perform well the predictive task in this context. This study intends to build a predictive framework for customer purchase behavior in the e-commerce context. This framework, known as CustOmer purchase pREdiction modeL (COREL), may be understood as a two-stage process. First, associations among products are investigated and exploited to predicate customer's motivations, i.e., to build a candidate product collection. Next, customer preferences for product features are learned and subsequently used to identify the candidate products most likely to be purchased. This study investigates three categories of product features and develops methods to detect customer preferences for each of these three categories. When a product purchased by a particular consumer is submitted to COREL, the program can return the top n products most likely to be purchased by that customer in the future. Experiments conducted on a real dataset show that customer preference for particular product features plays a key role in decision-making and that COREL greatly outperforms the baseline methods.