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Location perspective-based neighborhood-aware POI recommendation in location-based social networks

  • Lei GuoEmail author
  • Yufei Wen
  • Fangai Liu
Methodologies and Application
  • 17 Downloads

Abstract

As an effective way to help users find attractive locations and meet their individual needs, point-of-interest (POI) recommendation has become an important application in location-based social networks (LBSNs). Although the geographical influence has been reported as an effective factor for improving POI recommendation accuracy, previous work mainly models it from the user perspective instead of location perspective. Intuitively, neighboring POIs tend to be visited by similar users, which implies that modeling geographical relationships from a location perspective can simulate users’ behavior more reasonably. Moreover, different from traditional recommendation problems, users in LBSNs often express their interests only by checking in different POIs, which is a kind of implicit feedback. In other words, we can easily get the POIs that the users have visited, but it is hard to get the ones that the users do not like. We cannot use a common approach to distinguish these negative values directly. Based on the above observations, this work concentrates on exploiting the geographical relationships among POIs from a location perspective for implicit problem, where a location neighborhood-aware weighted probabilistic matrix factorization is proposed (L-WMF). To be specific, the weighted probabilistic matrix factorization (WMF) that can deal with implicit feedback is first introduced as our basic POI recommendation method. Then, we incorporate the geographical relationships among POIs into the WMF as the regularization terms to exploit the geographical characteristics from a location perspective. Finally, we conduct several experiments to evaluate our L-WMF method on two real-world datasets, and the experimental results indicate that the L-WMF is more effective and can reach better performance than other related methods.

Keywords

Social network Point-of-interest Weighted matrix factorization Implicit feedback 

Notes

Acknowledgements

This study was funded by the National Natural Science Foundation of China (Nos. 61602282, 61772321), the China Postdoctoral Science Foundation (No. 2016M602181) and the Innovation Foundation of Science and Technology Development Center of Ministry of Education and New H3C Group (No. 2017A15047).

Compliance with ethical standards

Conflict of interest

All authors declare that they have no conflict of interest.

Ethical approval

This article does not contain any studies with human participants or animals performed by any of the authors.

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Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Postdoctoral Research Station of Management Science and Engineering, School of BusinessShandong Normal UniversityJinanChina
  2. 2.School of BusinessShandong Normal UniversityJinanChina
  3. 3.School of Information Science and EngineeringShandong Normal UniversityJinanChina

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