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

• 成果类型: Article
• 署名作者: Guo, Junpeng; Zhang, Wenxiang; Fan, Weiguo; Li, Wenhua
• 署名单位: Tianjin University; Tianjin University; University of Iowa; Tianjin University
• 刊物名称: JOURNAL OF MANAGEMENT INFORMATION SYSTEMS
• ISSN/ISSBN: 0742-1222
• DOI: 10.1080/07421222.2018.1523564
• 发表日期: 2018
• 页码: 1121-1153
• 关键词: location; MOUTH; networks; INFORMATION; efficient; systems; IMPACT; MODEL

摘要

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.