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Probabilistic latent semantic analysis
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This paper proposes a method that analyzes the location log data of multiple users to recommend locations to be visited. The method uses our new topic model, called Geo Topic Model, that can jointly estimate both the user's interests and activity area hosting the user's home, office and other personal places. By explicitly modeling geographical features of locations and users, the user's interests in other features of locations, which we call latent topics, can be inferred effectively. The topic interests estimated by our model 1) lead to high accuracy in predicting visit behavior as driven by personal interests, 2) make possible the generation of recommendations when the user is in an unfamiliar area (e.g. sightseeing), and 3) enable the recommender system to suggest an interpretable representation of the user profile that can be customized by the user. Experiments are conducted using real location logs of landmark and restaurant visits to evaluate the recommendation performance of the proposed method in terms of the accuracy of predicting visit selections. We also show that our model can estimate latent features of locations such as art, nature and atmosphere as latent topics, and describe each user's preference based on them.