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Weighted Nonnegative Matrix Co-Tri-Factorization for Collaborative Prediction

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Book cover Advances in Machine Learning (ACML 2009)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 5828))

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Abstract

Collaborative prediction refers to the task of predicting user preferences on the basis of ratings by other users. Collaborative prediction suffers from the cold start problem where predictions of ratings for new items or predictions of new users’ preferences are required. Various methods have been developed to overcome this limitation, exploiting side information such as content information and demographic user data. In this paper we present a matrix factorization method for incorporating side information into collaborative prediction. We develop Weighted Nonnegative Matrix Co-Tri-Factorization (WNMCTF) where we jointly minimize weighted residuals, each of which involves a nonnegative 3-factor decomposition of target or side information matrix. Numerical experiments on MovieLens data confirm the useful behavior of WNMCTF when operating from a cold start.

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Authors and Affiliations

  1. Department of Computer Science, Pohang University of Science and Technology, San 31 Hyoja-dong, Nam-gu, Pohang, 790-784, Korea

    Jiho Yoo & Seungjin Choi

Authors
  1. Jiho Yoo
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  2. Seungjin Choi
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Editor information

Editors and Affiliations

  1. National Laboratory for Novel Software Technology, Nanjing University, 22 Hankou Road, 210093, Nanjing, China

    Zhi-Hua Zhou

  2. The Institute of Scientific and Industrial Research, Osaka University, 8-1 Mihogaoka, 567, Osaka, Ibaraki, Japan

    Takashi Washio

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© 2009 Springer-Verlag Berlin Heidelberg

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Yoo, J., Choi, S. (2009). Weighted Nonnegative Matrix Co-Tri-Factorization for Collaborative Prediction. In: Zhou, ZH., Washio, T. (eds) Advances in Machine Learning. ACML 2009. Lecture Notes in Computer Science(), vol 5828. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-05224-8_30

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  • DOI: https://doi.org/10.1007/978-3-642-05224-8_30

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-05223-1

  • Online ISBN: 978-3-642-05224-8

  • eBook Packages: Computer ScienceComputer Science (R0)

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