Sparse User Check-in Venue Prediction By Exploring Latent Decision Contexts From Location-Based Social Networks

Abstract

The proliferation of online Location-Based Social Networks (LBSN) has offered unprecedented opportunities for understanding fine-grained spatio-temporal behaviors of users and developing new location-aware applications. In this work, we focus on the problem of Sparse User Check-in Venue Prediction where the goal is to predict the next venue LBSN users will visit by exploiting their sparse online check-in traces and the latent decision contexts. While efforts have been made to predict users check-in traces on a LBSN, several important challenges still exist. First, check-in traces contributed by LBSN users are often too sparse to provide sufficient evidence for a reliable prediction, especially when the prediction space is huge (e.g., hundreds of thousands of venues in large cities). Second, the user s decision context on which venue to visit next is often latent and has not been incorporated by current venue prediction models. Third, the dynamic and non-deterministic dependency between check-ins is either ignored or replaced by a simplified consecutiveness assumption in existing solutions, leading to sub-optimal prediction results. In this work, we develop a Context-aware Sparse Check-in Venue Prediction (CSCVP) scheme inspired by natural language processing techniques to address the above challenges. In particular, CSCVP predicts the venue category information and explores the similarity between users to address data sparsity challenge by significantly reducing the prediction space. It also leverages the Probabilistic Latent Semantic Analysis (PLSA) model to incorporate the user decision context into the prediction model. Finally, we develop a novel Temporal Adaptive Ngram (TA-Ngram) model in CSCVP to capture the dynamic and non-deterministic dependency between check-ins. We evaluate CSCVP using three real-world LBSN datasets. The results show that our scheme significantly improves accuracy (30.9% improvement) of the state-of-the-art user check-in venue prediction solutions.