Recommendation method for extending subscription periods

Online stores providing subscription services need to extend user subscription periods as long as possible to increase their profits. Conventional recommendation methods recommend items that best coincide with user's interests to maximize the purchase probability, which does not necessarily contribute to extend subscription periods. We present a novel recommendation method for subscription services that maximizes the probability of the subscription period being extended. Our method finds frequent purchase patterns in the long subscription period users, and recommends items for a new user to simulate the found patterns. Using survival analysis techniques, we efficiently extract information from the log data for finding the patterns. Furthermore, we infer user's interests from purchase histories based on maximum entropy models, and use the interests to improve the recommendations. Since a longer subscription period is the result of greater user satisfaction, our method benefit...

• Type: Conference
• Year: 2006
• Where: KDD
• Authors: Tomoharu Iwata, Kazumi Saito, Takeshi Yamada
• Keywords: Data Mining, KDD 2006, Long Subscription Period, Longer Subscription Period, User Subscription Periods