Exploiting Geographical Neighborhood Characteristics for Location Recommendation
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 indiv...
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sg-smu-ink.sis_research-47722017-09-28T05:34:35Z Exploiting Geographical Neighborhood Characteristics for Location Recommendation LIU, Yong WEI, Wei SUN, Aixin MIAO, Chunyan 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. 2014-11-01T07:00:00Z text application/pdf https://ink.library.smu.edu.sg/sis_research/3770 info:doi/10.1145/2661829.2662002 https://ink.library.smu.edu.sg/context/sis_research/article/4772/viewcontent/ExploitGNCharacteristics_2014.pdf http://creativecommons.org/licenses/by-nc-nd/4.0/ Research Collection School Of Computing and Information Systems eng Institutional Knowledge at Singapore Management University Geographical Neighborhood Location Recommendation Matrix Factorization Location-based Social Networks Computer Sciences Databases and Information Systems |
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Geographical Neighborhood Location Recommendation Matrix Factorization Location-based Social Networks Computer Sciences Databases and Information Systems LIU, Yong WEI, Wei SUN, Aixin MIAO, Chunyan Exploiting Geographical Neighborhood Characteristics for Location Recommendation |
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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. |
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LIU, Yong WEI, Wei SUN, Aixin MIAO, Chunyan |
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LIU, Yong WEI, Wei SUN, Aixin MIAO, Chunyan |
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LIU, Yong |
title |
Exploiting Geographical Neighborhood Characteristics for Location Recommendation |
title_short |
Exploiting Geographical Neighborhood Characteristics for Location Recommendation |
title_full |
Exploiting Geographical Neighborhood Characteristics for Location Recommendation |
title_fullStr |
Exploiting Geographical Neighborhood Characteristics for Location Recommendation |
title_full_unstemmed |
Exploiting Geographical Neighborhood Characteristics for Location Recommendation |
title_sort |
exploiting geographical neighborhood characteristics for location recommendation |
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Institutional Knowledge at Singapore Management University |
publishDate |
2014 |
url |
https://ink.library.smu.edu.sg/sis_research/3770 https://ink.library.smu.edu.sg/context/sis_research/article/4772/viewcontent/ExploitGNCharacteristics_2014.pdf |
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