Cited by 85Recommender Systems and TechniquesHuman Mobility and Location-Based AnalysisInformation Retrieval and Search Behavior

Combining Geographical and Social Influences with Deep Learning for Personalized Point-of-Interest Recommendation

Guo, Junpeng, Zhang, Wenxiang, Fan, Weiguo, Li, Wenhua·2018·Journal of Management Information Systems

Abstract

Personalized point-of-interest (POI) recommendation is important to location-based social networks (LBSNs) for helping users to explore new places and for helping third-party services to launch targeted advertisements. Discovering effective features or representations from check-in data is the key to POI recommendation. Deep learning is a representation-learning method with multiple levels for discovering intrinsic features to better represent user preferences. We analyzed users’ check-in behavior in detail and developed a deep learning model to integrate geographical and social influences for POI recommendation tasks. We used a semi-restricted Boltzmann machine to model the geographical similarity and a conditional layer to model the social influence. Experiments with real-world LBSNs showed that our method performed better than other state-of-the-art methods. Theoretically, our study contributes to the effective usage of data science and analytics for social recommender system design. In practice, our results can be used to improve the quality of personalized POI recommendation services for websites and applications.

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Crossref · 10.1080/07421222.2018.1523564

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Authors
Guo, J., Zhang, W., Fan, W., & Li, W.
Affiliations
  • Tianjin University (CN)
  • University of Iowa (US)
Venue
Journal of Management Information Systems
Published
2018-10-02
Volume
35
Issue
4
Pages
1121-1153
ISSN
  • 0742-1222 (ISSN-L)
  • 1557-928X
OpenAlex
W2905146576
Cited by
85
References
60
Citations by year
2026: 4 · 2025: 5 · 2024: 11 · 2023: 19 · 2022: 14 · 2021: 15 · 2020: 12 · 2019: 4 · 2018: 1
FWCI
12.87
Type
journal article
Language
en
Open access
closed
Locations
Journal of Management Information Systems (publishedVersion)
Funders
National Natural Science Foundation of China (Grant # 71572122, Grant # 71671153, Grant # 71671154 and Grant # 71531013)
Topics
  • Physical Sciences › Computer Science › Information Systems › Recommender Systems and Techniques
  • Social Sciences › Transportation › Human Mobility and Location-Based Analysis
  • Physical Sciences › Computer Science › Information Systems › Information Retrieval and Search Behavior
Keywords
point-of-interest recommendation, location-based social networks, personalized recommendation, check-in data, social influence, user preference representation, social recommender systems

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