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Improving One-Class Collaborative Filtering with Manifold Regularization by Data-driven Feature Representation

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Advances in Knowledge Discovery and Data Mining (PAKDD 2017)

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Abstract

When considering additional features of users or items in a recommendation system, previous work focuses mainly on manually incorporating these features into original models. In this paper, manifold regularization is introduced to the well-known one-class collaborative filtering problem. To fully benefit from large unlabeled data, we design a data-driven framework, which learns a representation function by not only transferring raw features of users or items into latent ones but also directly linking the relation between the latent features and user behaviors. The framework is expected to bring cluster hypothesis from machine learning to recommendation, that is, more similar transferred features can bring more similar user behaviors. The experiments have been conducted on two real datasets. The results demonstrate that the learned representation through our framework can boost prediction performance significantly.

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Author information

Authors and Affiliations

  1. Department of Computer Science and Information Engineering, National Taiwan University, Taipei, Taiwan

    Yen-Chieh Lien & Pu-Jen Cheng

Authors
  1. Yen-Chieh Lien
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  2. Pu-Jen Cheng
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Corresponding author

Correspondence to Yen-Chieh Lien .

Editor information

Editors and Affiliations

  1. Kangwon National University, Chuncheon, Korea (Republic of)

    Jinho Kim

  2. Seoul National University, Seoul, Korea (Republic of)

    Kyuseok Shim

  3. University of Technology Sydney, Sydney, New South Wales, Australia

    Longbing Cao

  4. KAIST, Daejeon, Korea (Republic of)

    Jae-Gil Lee

  5. University of New South Wales, Sydney, New South Wales, Australia

    Xuemin Lin

  6. Kangwon National University, Chuncheon, Korea (Republic of)

    Yang-Sae Moon

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Lien, YC., Cheng, PJ. (2017). Improving One-Class Collaborative Filtering with Manifold Regularization by Data-driven Feature Representation. In: Kim, J., Shim, K., Cao, L., Lee, JG., Lin, X., Moon, YS. (eds) Advances in Knowledge Discovery and Data Mining. PAKDD 2017. Lecture Notes in Computer Science(), vol 10235. Springer, Cham. https://doi.org/10.1007/978-3-319-57529-2_44

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  • DOI: https://doi.org/10.1007/978-3-319-57529-2_44

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-57528-5

  • Online ISBN: 978-3-319-57529-2

  • eBook Packages: Computer ScienceComputer Science (R0)

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