Personalized Location Recommendations with Local Feature Awareness

Abstract

Location-based social networks (LBSNs) make it possible for servers to record users' location histories, mine their life patterns, and infer individual preferences. As an important component of LBSNs, recommender systems gained popularity in recent years. Recommender systems can automatically list candidate locations for users according to their preferences, which is different from traditional search methods. However, making effective recommendations suffers from data sparsity. In order to relieve this problem and achieve high effectiveness, we take context information into consideration and present a personalized location recommender system considering both user preference and local features in this paper. To be specific, we apply Labeled-LDA in user preference learning and local features inference processes, which are denoted as UL-LDA model and CL-LDA model, respectively. Because of this, we can make recommendations even on the condition that users are in a new city and have little information about the city. We evaluate our approach with extensive experiments on a large-scale Foursquare dataset. The experimental results clearly validate the effectiveness of our approach.