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Graph-Based Metric Embedding for Next POI Recommendation

  • Min Xie
  • Hongzhi YinEmail author
  • Fanjiang Xu
  • Hao Wang
  • Xiaofang Zhou
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10042)

Abstract

With the rapid prevalence of smart mobile devices and the dramatic proliferation of location-based social networks (LBSNs), point of interest (POI) recommendation has become an important means to help people discover attractive and interesting places. In this paper, we investigate the problem of next POI recommendation by considering the sequential influences of POIs, as a natural extension of the general POI recommendation, but it is more challenging than the general POI recommendation, due to that (1) users’ preferences are dynamic, and the next POI recommendation requires tracking the change of user preferences in a real-time manner; and (2) the prediction space is extremely large, with millions of distinct POIs as the next prediction target, which impedes the application of classical Markov chain models. In light of the above challenges, we propose a graph-based metric embedding model which converts POIs in a low dimensional metric and tracks the dynamics of user preferences in an efficient way. Besides, the knowledge of sequential patterns of users’ check-in behaviors can be exploited and encoded in the POI embedding, which avoid the time-consuming computation of the POI-POI transition matrix or even cube as the Markov chain-based recommender models have done. In other words, our proposed method effectively unifies dynamic user preferences and sequential influence via the POI embedding. Experiments on two real large-scale datasets demonstrate a significant improvement of our proposed models in terms of recommendation accuracy, compared with the state-of-the-art methods.

Keywords

Next POI recommendation Metric embedding Sequential influence Dynamic user preferences 

Notes

Acknowledgments

This work was supported by National Basic Research Program of China (2013CB329305), ARC Discovery Early Career Researcher Award (DE160100308) and ARC Discovery Project (DP140103171). It was also partially supported by National Natural Science Foundation of China (61572335, 61303164, 61402447 and 61502466), Development Plan of Outstanding Young Talent from Institute of Software, Chinese Academy of Sciences (ISCAS2014-JQ02) and Jiangsu Natural Science Foundation of China (BK20151223).

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Copyright information

© Springer International Publishing AG 2016

Authors and Affiliations

  • Min Xie
    • 1
  • Hongzhi Yin
    • 2
    Email author
  • Fanjiang Xu
    • 1
  • Hao Wang
    • 1
  • Xiaofang Zhou
    • 2
  1. 1.Science and Technology on Integrated Information System Laboratory, Institute of SoftwareChinese Academy of SciencesBeijingChina
  2. 2.School of Information Technology and Electrical EngineeringThe University of QueenslandBrisbaneAustralia

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