Combining Geographical and Social Influences with Deep Learning for Personalized Point-of-Interest Recommendation. Issue 4 (2nd October 2018)

• Record Type: Journal Article
• Authors: Guo, Junpeng; Zhang, Wenxiang; Fan, Weiguo; Li, Wenhua
• Is Part Of: Journal of management information systems. Volume 35:Issue 4(2018)
• Page Start: 1121
• Page End: 1153
• Publication Date: 2018-10-02
• Subjects: and phrases: location-based recommendations -- social recommendations -- POI recommendation -- deep learning -- auto-encoder -- semi-RBM -- Boltzmann machine -- geographical similarity -- social networks -- location-based commerce; Management information systems -- Periodicals; Management information systems; Periodicals; 658.4038011
• DOI: 10.1080/07421222.2018.1523564
• Languages: English
• ISSNs: 0742-1222

Abstract

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.