PGRank: Personalized Geographical Ranking for Point-of-Interest Recommendation

Haochao Ying, Liang Chen, Yuwen Xiong, Jian Wu

Abstract: 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.