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International Conference on User Modeling, Adaptation, and Personalization

UMAP 2015: User Modeling, Adaptation and Personalization pp 397–402Cite as

Exploiting Item and User Relationships for Recommender Systems

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Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 9146))

Abstract

Recommender systems have become a prevalent tool to cope with the information overload problem. The most well-known recommendation technique is collaborative filtering (CF), whereby a user’s preference can be predicted by her like-minded users. Data sparsity and cold start are two inherent and severe limitations of CF.

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Author information

Authors and Affiliations

  1. School of Computer Engineering, Nanyang Technological University, Singapore, Singapore

    Zhu Sun

Authors
  1. Zhu Sun
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Corresponding author

Correspondence to Zhu Sun .

Editor information

Editors and Affiliations

  1. Free University of Bozen-Bolzano, Bozen-Bolzano, Italy

    Francesco Ricci

  2. University of Sheffield, Sheffield, United Kingdom

    Kalina Bontcheva

  3. Trinity College Dublin, Dublin, Ireland

    Owen Conlan

  4. Trinity College Dublin, Dublin, Ireland

    Séamus Lawless

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© 2015 Springer International Publishing Switzerland

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Cite this paper

Sun, Z. (2015). Exploiting Item and User Relationships for Recommender Systems. In: Ricci, F., Bontcheva, K., Conlan, O., Lawless, S. (eds) User Modeling, Adaptation and Personalization. UMAP 2015. Lecture Notes in Computer Science(), vol 9146. Springer, Cham. https://doi.org/10.1007/978-3-319-20267-9_37

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  • DOI: https://doi.org/10.1007/978-3-319-20267-9_37

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-20266-2

  • Online ISBN: 978-3-319-20267-9

  • eBook Packages: Computer ScienceComputer Science (R0)

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