Combining Geographical and Social Influences with Deep Learning for Personalized Point-of-Interest Recommendation
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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A strategic framework for artificial intelligence in marketing
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“…(Antons and Breidbach 2018), adaptive systems can be used to personalize service to each consumer ’ s preference (Chung et al. 2009; Chung et al. 2016; Dzyabura and Hauser 2019; Liebman et al. 2019), and deep learning can be used to person-alize point-of-interest recommendations (Guo et al. 2018).”
Background
- Authors
- Guo, J., Zhang, W., Fan, W., & Li, W.
- Affiliations
- Tianjin University (CN)
- University of Iowa (US)
- Venue
- Journal of Management Information Systems
- Publisher
- Informa UK Limited
- 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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