Publication Type

Conference Proceeding Article

Version

Postprint

Publication Date

11-2014

Abstract

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.

Keywords

Geographical Neighborhood, Location Recommendation, Matrix Factorization, Location-based Social Networks

Discipline

Computer Sciences | Databases and Information Systems

Research Areas

Data Management and Analytics

Publication

CIKM '14: Proceedings of the 23rd ACM International Conference on Conference on Information and Knowledge Management, Shanghai, November 3-7

First Page

739

Last Page

748

ISBN

9781450325981

Identifier

10.1145/2661829.2662002

Publisher

ACM

City or Country

New York

Embargo Period

9-27-2017

Copyright Owner and License

Authors

Creative Commons License

Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License
This work is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License.

Additional URL

http://doi.org/10.1145/2661829.2662002

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