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Noise-tolerance matrix completion for location recommendation

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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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Notes

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

  2. https://dev.twitter.com/.

  3. https://developer.foursquare.com/.

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Acknowledgements

We want to thank Dr. Lei Chen for insightful discussions on matrix completion. We would also want to thank the editor and reviewers for their useful comments and suggestions for improving the paper. The work was supported in part by the National Science Foundation under Grant Nos. IIS-1213026 and CNS-1461926, Chinese National Natural Science Foundation under grant 91646116, and Scientific and Technological Support Project (Society) of Jiangsu Province (No. BE2016776).

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Authors and Affiliations

  1. School of Computer Science and Engineering, Nanjing University of Science and Technology, Nanjing, China

    Bin Xia, Qianmu Li & Hong Zhang

  2. School of Computing and Information Sciences, Florida International University, Miami, FL, USA

    Tao Li

  3. School of Computer Science, Nanjing University of Posts and Telecommunications, Nanjing, China

    Tao Li

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  1. Bin Xia
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  2. Tao Li
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  3. Qianmu Li
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  4. Hong Zhang
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Correspondence to Bin Xia or Tao Li.

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Responsible editor: G. Karypis.

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Xia, B., Li, T., Li, Q. et al. Noise-tolerance matrix completion for location recommendation. Data Min Knowl Disc 32, 1–24 (2018). https://doi.org/10.1007/s10618-017-0516-z

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