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LBRW: A Learning based Random Walk for Recommender Systems

LBRW: A Learning based Random Walk for Recommender Systems

Fatima Mourchid, Mohamed El Koutbi
Copyright: © 2015 |Volume: 6 |Issue: 3 |Pages: 20
ISSN: 1941-868X|EISSN: 1941-8698|EISBN13: 9781466676916|DOI: 10.4018/IJISSC.2015070102
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MLA

Mourchid, Fatima, and Mohamed El Koutbi. "LBRW: A Learning based Random Walk for Recommender Systems." IJISSC vol.6, no.3 2015: pp.15-34. http://doi.org/10.4018/IJISSC.2015070102

APA

Mourchid, F. & El Koutbi, M. (2015). LBRW: A Learning based Random Walk for Recommender Systems. International Journal of Information Systems and Social Change (IJISSC), 6(3), 15-34. http://doi.org/10.4018/IJISSC.2015070102

Chicago

Mourchid, Fatima, and Mohamed El Koutbi. "LBRW: A Learning based Random Walk for Recommender Systems," International Journal of Information Systems and Social Change (IJISSC) 6, no.3: 15-34. http://doi.org/10.4018/IJISSC.2015070102

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Abstract

Location-based social networks (LBSNs) have witnessed a great expansion as an attractive form of social media. LBSNs allow users to “check-in” at geographical locations and share this information with friends. Indeed, with the spatial, temporal and social aspects of user patterns provided by LBSNs data, researchers have a promising opportunity for understanding human mobility dynamics, with the purpose of designing new generation mobile applications, including context-aware advertising and city-wide sensing applications. In this paper, the authors introduce a learning based random walk model (LBRW) combining user interests and “mobility homophily” for location recommendation in LBSNs. These properties are observed from a real-world Location-Based Social Networks (LBSNs) dataset. The authors present experimental evidence that validates LBRW and demonstrates the power of these inferred properties in improving location recommendation performance.

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