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World Wide Web

, Volume 22, Issue 5, pp 2209–2224 | Cite as

Time-aware metric embedding with asymmetric projection for successive POI recommendation

  • Haochao Ying
  • Jian Wu
  • Guandong XuEmail author
  • Yanchi Liu
  • Tingting Liang
  • Xiao Zhang
  • Hui Xiong
Article
  • 396 Downloads
Part of the following topical collections:
  1. Special Issue on Big Data Management and Intelligent Analytics

Abstract

Successive Point-of-Interest (POI) recommendation aims to recommend next POIs for a given user based on this user’s current location. Indeed, with the rapid growth of Location-based Social Networks (LBSNs), successive POI recommendation has become an important and challenging task, since it can help to meet users’ dynamic interests based on their recent check-in behaviors. While some efforts have been made for this task, most of them do not capture the following properties: 1) The transition between consecutive POIs in user check-in sequences presents asymmetric property, however existing approaches usually assume the forward and backward transition probabilities between a POI pair are symmetric. 2) Users usually prefer different successive POIs at different time, but most existing studies do not consider this dynamic factor. To this end, in this paper, we propose a time-aware metric embedding approach with asymmetric projection (referred to as MEAP-T) for successive POI recommendation, which takes the above two properties into consideration. In addition, we exploit three latent Euclidean spaces to project the POI-POI, POI-user, and POI-time relationships. Finally, the experimental results on two real-world datasets show MEAP-T outperforms the state-of-the-art methods in terms of both precision and recall.

Keywords

Successive POI recommendation Metric embedding Asymmetric projection Temporal influence 

Notes

Acknowledgments

This research was partially supported by the Ministry of Education of China under grant of No.2017PT18, the Natural Science Foundation of China under grant of No. 61379119 and No. 61672453, the WE-DOCTOR company under grant of No. 124000-11110, the Zhejiang University Education Foundation under grant of No. K17-511120-017, and the Australia Research Council under grant of No. LP140100937.

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Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  1. 1.College of Computer Science & TechnologyZhejiang UniversityHangzhouChina
  2. 2.Advanced Analytics InstituteUniversity of Technology SydneySydneyAustralia
  3. 3.Management Science & Information SystemsRutgers UniversityNewarkUSA
  4. 4.State Key Laboratory for Novel Software TechnologyNanjing UniversityNanjingChina

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