REGULA

Abstract

In this paper, we address the problem of recommending new locations to the users of a Location Based Social Network (LBSN). LBSNs are social and physical information-rich networks that incorporate mobility patterns and social ties of humans. Most of the existing recommender systems are build on variants of graph-based techniques that utilize complete knowledge of location history and social ties of all users. Therefore, these recommender systems are computationally expensive for large scale LBSNs. Further, these systems do not take into account the mobility habits of humans. Recent studies on human mobility patterns have highlighted that people frequently visit a set of locations and go to places closer to them. In this paper, we validate the existence of these human mobility aspects in LBSN through the analysis of user check-in behavior and derive a set of observations. Further, we propose REGULA-- A location recommendation algorithm that exploits three behavior patterns of humans: 1) People regularly (or habitually) visit a set of locations 2) People go to places close to these regularly visited locations and 3) People are more likely to visit places that were recently visited by others like friends. Using these behavior patterns, REGULA minimizes the computational complexity by reducing the set of candidate locations to recommend. We evaluate the performance of REGULA by employing two large scale LBSN datasets: Gowalla and Brightkite. Based on our results, we show that REGULA outperforms existing state of the art recommendation algorithms for LBSNs while reducing the complexity.