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Collaborative Item Embedding Model for Implicit Feedback Data

  • ThaiBinh NguyenEmail author
  • Kenro Aihara
  • Atsuhiro Takasu
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10360)

Abstract

Collaborative filtering is the most popular approach for recommender systems. One way to perform collaborative filtering is matrix factorization, which characterizes user preferences and item attributes using latent vectors. These latent vectors are good at capturing global features of users and items but are not strong in capturing local relationships between users or between items. In this work, we propose a method to extract the relationships between items and embed them into the latent vectors of the factorization model. This combines two worlds: matrix factorization for collaborative filtering and item embedding, a similar concept to word embedding in language processing. Our experiments on three real-world datasets show that our proposed method outperforms competing methods on top-n recommendation tasks.

Keywords

Recommender system Collaborative filtering Matrix factorization Item embedding 

Notes

Acknowledgments

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

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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • ThaiBinh Nguyen
    • 1
    Email author
  • Kenro Aihara
    • 2
  • Atsuhiro Takasu
    • 2
  1. 1.Department of InformaticsSOKENDAI (The Graduate University for Advanced Studies)HayamaJapan
  2. 2.National Institute of InformaticsChiyodaJapan

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