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Cross-Domain Recommendation via Deep Domain Adaptation

  • Heishiro KanagawaEmail author
  • Hayato Kobayashi
  • Nobuyuki Shimizu
  • Yukihiro Tagami
  • Taiji Suzuki
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11438)

Abstract

The behavior of users in certain services indicates their preferences, which may be used to make recommendations for other services they have never used. However, the cross-domain relation between items and user preferences is not simple, especially when there are few or no common users and items across domains. We propose a content-based cross-domain recommendation method for cold-start users that does not require user- or item-overlap. We formulate recommendations as an extreme classification task, and the problem is treated as an instance of unsupervised domain adaptation. We assess the performance of the approach in experiments on large datasets collected from Yahoo! JAPAN video and news services and find that it outperforms several baseline methods including a cross-domain collaborative filtering method.

Keywords

Cross-domain recommendation Deep domain adaptation 

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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Heishiro Kanagawa
    • 1
    Email author
  • Hayato Kobayashi
    • 2
    • 4
  • Nobuyuki Shimizu
    • 2
  • Yukihiro Tagami
    • 2
  • Taiji Suzuki
    • 3
    • 4
  1. 1.Gatsby Unit, UCLLondonUK
  2. 2.Yahoo Japan CorporationTokyoJapan
  3. 3.The University of TokyoTokyoJapan
  4. 4.RIKEN Center for Advanced Intelligence ProjectTokyoJapan

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