Personalized Ranking Metric Embedding for Next New POI Recommendation

Shanshan Feng, Xutao Li, Yifeng Zeng, Gao Cong, Yeow Meng Chee, Quan Yuan

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

424 Citations (Scopus)
32 Downloads (Pure)


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.
Original languageEnglish
Title of host publicationProceedings of the twenty-fourth international joint conference on artificial intelligence
EditorsQiang Yang, Michael J. Wooldridge
Place of PublicationPalo Alto
PublisherAssociation for the Advancement of Artificial Intelligence (AAAI)
Number of pages7
ISBN (Print)9781577357384
Publication statusPublished - 1 Nov 2015
Externally publishedYes
EventThe Twenty-Fourth International Joint Conference on Artificial Intelligence -
Duration: 25 Jul 201531 Jul 2015


ConferenceThe Twenty-Fourth International Joint Conference on Artificial Intelligence
Abbreviated titleIJCAI


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