Personalized Ranking Metric Embedding for Next New POI Recommendation

Feng, Shanshan, Li, Xutao, Zeng, Yifeng, Cong, Gao, Chee, Yeow Meng and Yuan, Quan (2015) Personalized Ranking Metric Embedding for Next New POI Recommendation. In: Proceedings of the twenty-fourth international joint conference on artificial intelligence. AAAI Press, Palo Alto, pp. 2069-2075. ISBN 9781577357384

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Abstract

The rapidly growing of Location-based Social Networks (LBSNs) provides a vast amount of check-in data, which enables many services, e.g., point-of-interest (POI) recommendation. In this paper, we study the next new POI recommendation problem in which new POIs with respect to users' current location are to be recommended. The challenge lies in the difficulty in precisely learning users' sequential information and personalizing the recommendation model. To this end, we resort to the Metric Embedding method for the recommendation, which avoids drawbacks of the Matrix Factorization technique. We propose a personalized ranking metric embedding method (PRME) to model personalized check-in sequences. We further develop a PRME-G model, which integrates sequential information, individual preference, and geographical influence, to improve the recommendation performance. Experiments on two real-world LBSN datasets demonstrate that our new algorithm outperforms the state-of-the-art next POI recommendation methods.

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