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Exploiting Geographical Neighborhood Characteristics for Location Recommendation

Country : Singapore
Department : Singapore Management University
Project Title : Exploiting Geographical Neighborhood Characteristics for Location Recommendation
Researcher : WEI, Wei , SUN, Aixin , LIU, Yong , MIAO, Chunyan
Keyword : Matrix Factorization , Location Recommendation , Geographical Neighborhood , Computer Sciences , Location-based Social Networks , Databases and Information Systems
Publisher : Institutional Knowledge at Singapore Management University
Year End : 2014
Identifier : https://ink.library.smu.edu.sg/cgi/viewcontent.cgi?article=4772&context=sis_research , https://ink.library.smu.edu.sg/sis_research/3770
Source : Research Collection School Of Computing and Information Systems
Abstract / Description :

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.

References

WEI, Wei and others / et al. (2014). Exploiting Geographical Neighborhood Characteristics for Location Recommendation.  Singapore: Singapore Management University.
WEI, Wei and others / et al. 2014. "Exploiting Geographical Neighborhood Characteristics for Location Recommendation".  Singapore: Singapore Management University.
WEI, Wei and others / et al. "Exploiting Geographical Neighborhood Characteristics for Location Recommendation."  Singapore: Singapore Management University, 2014. Print.
WEI, Wei and others / et al. Exploiting Geographical Neighborhood Characteristics for Location Recommendation. Singapore: Singapore Management University; 2014.

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