PRME-GTS: A New Successive POI Recommendation Model with Temporal and Social Influences

  • Rubai Mao
  • Zhe Han
  • Zitu Liu
  • Yong LiuEmail author
  • Xingfeng Lv
  • Ping Xuan
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11888)


Successive point-of-interest (POI) recommendation is an important research task which can recommend new POIs the user has not visited before. However, the existing researches for new successive POI recommendation ignore the integration of time information and social relations information which can improve the prediction of the system. In order to solve this problem, we propose a new recommendation model called PRME-GTS that incorporates social relations and temporal information in this paper. It can models the relations between users, temporal information, points of interest, and social information, which is based on the framework of pair-wise ranking metric embedding. Experimental results on the two datasets demonstrate that employing temporal information and social relations information can effectively improve the performance of the successive point-of-interest (POI) recommendation.


Social networks POI recommendation Metric embedding 



This work was supported by the National Natural Science Foundation of China (No. 61972135, No. 61602159), the Natural Science Foundation of Heilongjiang Province (No. F201430), the Innovation Talents Project of Science and Technology Bureau of Harbin (No. 2017RAQXJ094, No. 2017RAQXJ131), and the fundamental research funds of universities in Heilongjiang Province, special fund of Heilongjiang University (No. HDJCCX-201608, No. KJCX201815, No. KJCX201816).


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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Rubai Mao
    • 1
  • Zhe Han
    • 1
  • Zitu Liu
    • 1
  • Yong Liu
    • 1
    Email author
  • Xingfeng Lv
    • 1
  • Ping Xuan
    • 1
  1. 1.HeiLongJiang UniversityHarbinChina

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