Evaluating collaborative filtering recommender systems
ACM Transactions on Information Systems (TOIS)
Improving recommendation lists through topic diversification
WWW '05 Proceedings of the 14th international conference on World Wide Web
Recommendation method for extending subscription periods
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
A framework for WWW user activity analysis based on user interest
Knowledge-Based Systems
Prediction of social bookmarking based on a behavior transition model
Proceedings of the 2010 ACM Symposium on Applied Computing
Recommendation boosted query propagation in the social network
SocInfo'10 Proceedings of the Second international conference on Social informatics
Towards group behavioral reason mining
Expert Systems with Applications: An International Journal
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We propose a model for user purchase behavior in online stores that provide recommendation services. We model the purchase probability given recommendations for each user based on the maximum entropy principle using features that deal with recommendations and user interests. The proposed model enable us to measure the effect of recommendations on user purchase behavior, and the effect can be used to evaluate recommender systems. We show the validity of our model using the log data of an online cartoon distribution service, and measure the recommendation effects for evaluating the recommender system.