Journal article
Combining Geographical and Social Influences with Deep Learning for Personalized Point-of-Interest Recommendation
Journal of management information systems, Vol.35(4), pp.1121-1153
10/02/2018
DOI: 10.1080/07421222.2018.1523564
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.
Details
- Title: Subtitle
- Combining Geographical and Social Influences with Deep Learning for Personalized Point-of-Interest Recommendation
- Creators
- Junpeng Guo - Tianjin UniversityWenxiang Zhang - Tianjin UniversityWeiguo Fan - Univ Iowa, Business Analyt, Iowa City, IA 52242 USAWenhua Li - Tianjin University
- Resource Type
- Journal article
- Publication Details
- Journal of management information systems, Vol.35(4), pp.1121-1153
- Publisher
- Taylor & Francis
- DOI
- 10.1080/07421222.2018.1523564
- ISSN
- 0742-1222
- eISSN
- 1557-928X
- Number of pages
- 33
- Grant note
- DOI: 10.13039/501100001809, name: National Natural Science Foundation of China, award: Grant # 71572122, Grant # 71671153, Grant # 71671154 and Grant # 71531013
- Language
- English
- Date published
- 10/02/2018
- Academic Unit
- Business Analytics
- Record Identifier
- 9984380553202771
Metrics
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