Conference Proceeding Article
Geographical characteristics derived from the historical check-in data have been reported effective in improving location recommendation accuracy. However, previous studies mainly exploit geographical characteristics from a user’s perspective, via modeling the geographical distribution of each individual user’s check-ins. In this paper, we are interested in exploiting geographical characteristics from a location perspective, by modeling the geographical neighborhood of a location. The neighborhood is modeled at two levels: the instance-level neighborhood defined by a few nearest neighbors of the location, and the region-level neighborhood for the geographical region where the location exists. We propose a novel recommendation approach, namely Instance-Region Neighborhood Matrix Factorization (IRenMF), which exploits two levels of geographical neighborhood characteristics: a) instance-level characteristics, i.e., nearest neighboring locations tend to share more similar user preferences; and b) region-level characteristics, i.e., locations in the same geographical region may share similar user preferences. In IRenMF, the two levels of geographical characteristics are naturally incorporated into the learning of latent features of users and locations, so that IRenMF predicts users’ preferences on locations more accurately. Extensive experiments on the real data collected from Gowalla, a popular LBSN, demonstrate the effectiveness and advantages of our approach.
Geographical Neighborhood, Location Recommendation, Matrix Factorization, Location-based Social Networks
Computer Sciences | Databases and Information Systems
Data Management and Analytics
CIKM '14: Proceedings of the 23rd ACM International Conference on Conference on Information and Knowledge Management, Shanghai, November 3-7
City or Country
LIU, Yong; WEI, Wei; SUN, Aixin; and MIAO, Chunyan.
Exploiting Geographical Neighborhood Characteristics for Location Recommendation. (2014). CIKM '14: Proceedings of the 23rd ACM International Conference on Conference on Information and Knowledge Management, Shanghai, November 3-7. 739-748. Research Collection School Of Information Systems.
Available at: http://ink.library.smu.edu.sg/sis_research/3770
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