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A Probabilistic Model for the Cold-Start Problem in Rating Prediction Using Click Data

  • ThaiBinh NguyenEmail author
  • Atsuhiro Takasu
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10638)

Abstract

One of the most efficient methods in collaborative filtering is matrix factorization, which finds the latent vector representations of users and items based on the ratings of users to items. However, a matrix factorization based algorithm suffers from the cold-start problem: it cannot find latent vectors for items to which previous ratings are not available. This paper utilizes click data, which can be collected in abundance, to address the cold-start problem. We propose a probabilistic item embedding model that learns item representations from click data, and a model named EMB-MF, that connects it with a probabilistic matrix factorization for rating prediction. The experiments on three real-world datasets demonstrate that the proposed model is not only effective in recommending items with no previous ratings, but also outperforms competing methods, especially when the data is very sparse.

Keywords

Recommender system Collaborative filtering Item embedding Matrix factorization 

Notes

Acknowledgments

This work was supported by a JSPS Grant-in-Aid for Scientific Research (B) (15H02789, 15H02703).

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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  1. 1.Department of InformaticsSOKENDAI (The Graduate University for Advanced Studies)TokyoJapan
  2. 2.National Institute of InformaticsTokyoJapan

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