Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/96701
Title: Time-aware point-of-interest recommendation
Authors: Yuan, Quan
Cong, Gao
Ma, Zongyang
Sun, Aixin
Thalmann, Nadia Magnenat
Keywords: DRNTU::Engineering::Computer science and engineering
Issue Date: 2013
Source: Yuan, Q., Cong, G., Ma, Z., Sun, A., & Nadia, M-T. (2013). Time-aware point-of-interest recommendation. Proceedings of the 36th international ACM SIGIR conference on Research and development in information retrieval - SIGIR '13, 363-372.
Abstract: The 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 recommend places where users have not visited before. Several techniques have been recently proposed for the recommendation service. However, no existing work has considered the temporal information for POI recommendations in LBSNs. We believe that time plays an important role in POI recommendations because most users tend to visit different places at different time in a day, \eg visiting a restaurant at noon and visiting a bar at night. In this paper, we define a new problem, namely, the time-aware POI recommendation, to recommend POIs for a given user at a specified time in a day. To solve the problem, we develop a collaborative recommendation model that is able to incorporate temporal information. Moreover, based on the observation that users tend to visit nearby POIs, we further enhance the recommendation model by considering geographical information. Our experimental results on two real-world datasets show that the proposed approach outperforms the state-of-the-art POI recommendation methods substantially.
URI: https://hdl.handle.net/10356/96701
http://hdl.handle.net/10220/18059
DOI: 10.1145/2484028.2484030
Fulltext Permission: none
Fulltext Availability: No Fulltext
Appears in Collections:SCSE Conference Papers

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