PR-RCUC: A POI Recommendation Model Using Region-Based Collaborative Filtering and User-Based Mobile Context

Recent years have witnessed the rapid prevalence of big data and it is necessary for mobile application to filter out information for users. As a significant means of information retrieval, recommendation system that recommends a ranked list of items to users according to their preferences has become a key functionality in Location-Based Social Networks (LBSNs). Point of interest (POI) recommendation that aims to recommend satisfactory locations that users may be interested in plays an important role in LBSNs. However, the traditional POI recommendation uses the original user-POI matrix, which faces a huge challenge of data sparsity because most users just check in a few POIs in their phones. Moreover, it is hard for POI recommendation to give reasonable explanations on why user will visit these locations that we recommend. Therefore, in terms of the challenges mentioned above, we propose a new POI recommendation model called PR-RCUC that uses region-based collaborative filtering and user-based mobile context. Firstly, we cluster locations into different regions and enhance the traditional collaborative filtering with region factor. Secondly, we capture the preferences of users on mobile context such as geographical distance and location category. Thirdly, by combing the two parts we present, we finish the final computation of prediction score and recommend Top-K locations to users. The results of experiments on two real-world datasets collected from Foursquare demonstrate the PR-RCUC model outperforms some popular recommendation algorithms and achieves our expected goal.