PGRank: Personalized Geographical Ranking for Point-of-Interest Recommendation.
WWW '16: 25th International World Wide Web Conference Montréal Québec Canada April, 2016(2016)
摘要
Point-of-interest (POI) recommendation has become more and more important, since it could discover user behavior pattern and find interesting venues for them. To address this problem, we propose a rank-based method, PGRank, which integrates user geographical preference and latent preference into Bayesian personalized ranking framework. The experimental results on a real dataset show its effective.
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