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Poster Papers

Track: Social Networks

Paper Title:
Modeling User Behavior in Recommender Systems based on Maximum Entropy

Authors:

  • Tomoharu Iwata (NTT)
  • Kazumi Saito (NTT)
  • Takeshi Yamada (NTT)

Abstract:
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.

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