Fine-Gained Location Recommendation Based on User Textual Reviews in LBSNs

As user-generated reviews from Location Based Social Networks (LBSNs) are becoming increasingly pervasive, exploiting sentiment analysis based on user’s textual reviews for location recommendation has become a popular approach due to its explainable property and high prediction accuracy. However, the inherent limitations of existing methods make it difficult to discover what aspects that a user cared most about when visiting a location. In this study, we propose a fine-gained location recommendation model by jointly exploiting user’s textual reviews and ratings from LBSNs, which considers not only the direct rating that a user would score on a location but also the compatibility between user’s interested features and location’s high-quality features. Specifically, the proposed recommendation model consists of three steps: (1) extracting feature-sentiment pairs from user’s textual reviews; (2) learning to rank features using an Elo-based scheme; (3) making fine-gained location recommendation. Experiment results demonstrate that our proposed model can improve the recommendation performance compared with several state-of-the-art methods.