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Modeling user behavior in recommender systems based on maximum entropy

9 years 11 months ago
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
Tomoharu Iwata, Kazumi Saito, Takeshi Yamada
Added 21 Nov 2009
Updated 21 Nov 2009
Type Conference
Year 2007
Where WWW
Authors Tomoharu Iwata, Kazumi Saito, Takeshi Yamada
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