Modeling user behavior in recommender systems based on maximum entropy

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. Categories and Subject Descriptors H.2.8 [Database Management]: Database Applications-Data Mining; H.3.3 [Information Storage and Retrieval]: Information Retrieval and Search--Information Filtering General Terms Algorithms,Experimentation Keywords recommendation, user model, maximum entropy principle

• Type: Conference
• Year: 2007
• Where: WWW
• Authors: Tomoharu Iwata, Kazumi Saito, Takeshi Yamada
• Keywords: Internet Technology, Maximum Entropy Principle, User Model, User Purchase Behavior, WWW 2007