On the limited memory BFGS method for large scale optimization
Mathematical Programming: Series A and B
GroupLens: an open architecture for collaborative filtering of netnews
CSCW '94 Proceedings of the 1994 ACM conference on Computer supported cooperative work
Statistics and data mining techniques for lifetime value modeling
KDD '99 Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining
Estimating campaign benefits and modeling lift
KDD '99 Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining
Content-based book recommending using learning for text categorization
DL '00 Proceedings of the fifth ACM conference on Digital libraries
E-Commerce Recommendation Applications
Data Mining and Knowledge Discovery
Customer Lifetime Value Models for Decision Support
Data Mining and Knowledge Discovery
Data Mining Techniques: For Marketing, Sales, and Customer Relationship Management
Data Mining Techniques: For Marketing, Sales, and Customer Relationship Management
A maximum entropy web recommendation system: combining collaborative and content features
Proceedings of the eleventh ACM SIGKDD international conference on Knowledge discovery in data mining
A novel evolutionary data mining algorithm with applications to churn prediction
IEEE Transactions on Evolutionary Computation
IEEE Transactions on Neural Networks
Modeling user behavior in recommender systems based on maximum entropy
Proceedings of the 16th international conference on World Wide Web
Computers and Electrical Engineering
Topic model for analyzing purchase data with price information
Data Mining and Knowledge Discovery
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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 benefits users as well as online stores. We evaluate our method using the real log data of an online cartoon distribution service for cell-phone in Japan.