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Cross-Domain Recommendation via Deep Domain Adaptation

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Advances in Information Retrieval (ECIR 2019)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 11438))

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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.

This work was conducted during the first author’s internship at Yahoo! JAPAN.

TS was partially supported by MEXT Kakenhi (18H03201) and JST-CREST.

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Notes

  1. 1.

    Superscript X denotes that the data is missing labels associated to their input vectors.

  2. 2.

    The unit size of each hidden layer is as follows: \(E_c = E_p^S = E_p^T = (256 - 128 - 128 - 64)\), \(D = (128 - 128 - 256)\), and \(G = (256 - 256 - 256 - 64)\). Left is the input.

  3. 3.

    We train a classifier that detects the domain of the input represented by the shared encoder and test the classification accuracy. This is due to the adversarial training.

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Author information

Authors and Affiliations

  1. Gatsby Unit, UCL, London, UK

    Heishiro Kanagawa

  2. Yahoo Japan Corporation, Tokyo, Japan

    Hayato Kobayashi, Nobuyuki Shimizu & Yukihiro Tagami

  3. The University of Tokyo, Tokyo, Japan

    Taiji Suzuki

  4. RIKEN Center for Advanced Intelligence Project, Tokyo, Japan

    Hayato Kobayashi & Taiji Suzuki

Authors
  1. Heishiro Kanagawa
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  2. Hayato Kobayashi
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  3. Nobuyuki Shimizu
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  4. Yukihiro Tagami
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  5. Taiji Suzuki
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Corresponding author

Correspondence to Heishiro Kanagawa .

Editor information

Editors and Affiliations

  1. University of Strathclyde, Glasgow, UK

    Leif Azzopardi

  2. Bauhaus Universität Weimar, Weimar, Germany

    Benno Stein

  3. Universität Duisburg-Essen, Duisburg, Germany

    Norbert Fuhr

  4. GESIS - Leibniz Institute for the Social Sciences, Cologne, Germany

    Philipp Mayr

  5. Delft University of Technology, Delft, The Netherlands

    Claudia Hauff

  6. University of Twente, Enschede, The Netherlands

    Djoerd Hiemstra

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Kanagawa, H., Kobayashi, H., Shimizu, N., Tagami, Y., Suzuki, T. (2019). Cross-Domain Recommendation via Deep Domain Adaptation. In: Azzopardi, L., Stein, B., Fuhr, N., Mayr, P., Hauff, C., Hiemstra, D. (eds) Advances in Information Retrieval. ECIR 2019. Lecture Notes in Computer Science(), vol 11438. Springer, Cham. https://doi.org/10.1007/978-3-030-15719-7_3

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  • DOI: https://doi.org/10.1007/978-3-030-15719-7_3

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