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.
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Index Terms
REGULA: Utilizing the Regularity of Human Mobility for Location Recommendation
Recommendations
Time-aware point-of-interest recommendation
SIGIR '13: Proceedings of the 36th international ACM SIGIR conference on Research and development in information retrievalThe availability of user check-in data in large volume from the rapid growing location based social networks (LBSNs) enables many important location-aware services to users. Point-of-interest (POI) recommendation is one of such services, which is to ...
A HITS-based POI recommendation algorithm for location-based social networks
ASONAM '13: Proceedings of the 2013 IEEE/ACM International Conference on Advances in Social Networks Analysis and MiningLocation-Based Social Networks (LBSNs), (also called as Geo-Social Networks), has been attracting more and more users by providing services that integrate social activities with location information. LBSN systems usually provide support for indicating ...
Graph-based Point-of-interest Recommendation with Geographical and Temporal Influences
CIKM '14: Proceedings of the 23rd ACM International Conference on Conference on Information and Knowledge ManagementThe availability of user check-in data in large volume from the rapid growing location-based social networks (LBSNs) enables a number of important location-aware services. Point-of-interest (POI) recommendation is one of such services, which is to ...
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