Recommender systems are widely used in online stores because they are expected to improve both user convenience and online store profit. As such, a number of recommendation methods have been proposed in recent years. Functions required for recommender systems vary significantly depending on business models or/and situations. Although an online store can acquire various kinds of information about user behaviors such as purchase history and visiting time of users, this information has not yet been fully used to fulfill the diverse requirements. In this thesis, we propose probabilistic user behavior models for use in online stores to tailor recommender systems to diverse requirements efficiently using various kinds of user behavior information. The probabilistic model-based approach allows us to systematically integrate heterogeneous user behavior information using rules of the probability theory. In particular, we consider three requirements for recommende
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