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Data Mining and Knowledge Discovery

Erschienen in:

05.06.2017

Noise-tolerance matrix completion for location recommendation

verfasst von: Bin Xia, Tao Li, Qianmu Li, Hong Zhang

Erschienen in: Data Mining and Knowledge Discovery | Ausgabe 1/2018

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Abstract

Due to the sharply increasing number of users and venues in Location-Based Social Networks, it becomes a big challenge to provide recommendations which match users’ preferences. Furthermore, the sparse data and skew distribution (i.e., structural noise) also worsen the coverage and accuracy of recommendations. This problem is prevalent in traditional recommender methods since they assume that the collected data truly reflect users’ preferences. To overcome the limitation of current recommenders, it is imperative to explore an effective strategy, which can accurately provide recommendations while tolerating the structural noise. However, few study concentrates on the process of noisy data in the recommender system, even recent matrix-completion algorithms. In this paper, we cast the location recommendation as a mathematical matrix-completion problem and propose a robust algorithm named Linearized Bregman Iteration for Matrix Completion (LBIMC), which can effectively recover the user-location matrix considering structural noise and provide recommendations based solely on check-in records. Our experiments are conducted by an amount of check-in data from Foursquare, and the results demonstrate the effectiveness of LBIMC.

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This service has been separated from Foursquare and was integrated into Swarm APP in May, 2014.

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Titel: Noise-tolerance matrix completion for location recommendation
verfasst von: Bin Xia
Tao Li
Qianmu Li
Hong Zhang
Publikationsdatum: 05.06.2017
Verlag: Springer US
Erschienen in: Data Mining and Knowledge Discovery / Ausgabe 1/2018
Print ISSN: 1384-5810
Elektronische ISSN: 1573-756X
DOI: https://doi.org/10.1007/s10618-017-0516-z

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