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Enhancing Collaborative Filtering with Multi-label Classification

  • Yang ZhouEmail author
  • Ling Liu
  • Qi Zhang
  • Kisung Lee
  • Balaji Palanisamy
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11917)

Abstract

This paper presents a multi-label classification based CF framework, MLCF, which improves the quality of recommendation in the presence of data sparsity by learning over a heterogeneous information network consisting of a rating bipartite graph, a user graph and an item graph. MLCF is novel by three unique features. First, we explore the latent correlations among users and items w.r.t. a given set of K semantic categories beyond user-item ratings by employing multi-label clustering of items, and multi-label classification of users and rating-based similarities on the heterogeneous network. Second, based on the user/item/similarity multi-label clustering/classification, we propose a fine-grained multi-label classification based rating similarity measure to capture the class-specific relationships between users by introducing a novel concept of vertex-edge homophily. Third but not the least, we propose to integrate two kinds of multi-label classification based CF models focusing on rating and social information into a unified prediction model.

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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Yang Zhou
    • 1
    Email author
  • Ling Liu
    • 2
  • Qi Zhang
    • 3
  • Kisung Lee
    • 4
  • Balaji Palanisamy
    • 5
  1. 1.Auburn UniversityAuburnUSA
  2. 2.Georgia Institute of TechnologyAtlantaUSA
  3. 3.IBM T.J. Watson Research CenterYorktown HeightsUSA
  4. 4.Louisiana State UniversityBaton RougeUSA
  5. 5.University of PittsburghPittsburghUSA

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