Venue Appropriateness Prediction for Personalized Context-Aware Venue Suggestion

Personalized context-aware venue suggestion plays a critical role in satisfying the users' needs on location-based social networks (LBSNs). In this paper, we present a set of novel scores to measure the similarity between a user and a candidate venue in a new city. The scores are based on user's history of preferences in other cities as well as user's context. We address the data sparsity problem in venue recommendation with the aid of a proposed approach to predict contextually appropriate places. Furthermore, we show how to incorporate different scores to improve the performance of recommendation. The experimental results of our participation in the TREC 2016 Contextual Suggestion track show that our approach beats state-of-the-art strategies.