Collaborative filtering meets next check-in location prediction

Authors:
Defu Lian;Vincent W. Zheng;Xing Xie
Affiliations:
University of Science and Technology of China, Beijing, China;Advanced Digital Sciences Center, Singapore, Singapore, Singapore;Microsoft Research Asia, Beijing, China, Beijing, China
Venue:
Proceedings of the 22nd international conference on World Wide Web companion
Year:
2013

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Abstract

With the increasing popularity of Location-based Social Networks, a vast amount of location check-ins have been accumulated. Though location prediction in terms of check-ins has been recently studied, the phenomena that users often check in novel locations has not been addressed. To this end, in this paper, we leveraged collaborative filtering techniques for check-in location prediction and proposed a short- and long-term preference model. We extensively evaluated it on two large-scale check-in datasets from Gowalla and Dianping with 6M and 1M check-ins, respectively, and showed that the proposed model can outperform the competing baselines.