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Deep Learning for Recommender Systems

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Recommender Systems Handbook

Abstract

Deep neural networks have been serving as the main driving force for the emergence of cutting-edge applications in many areas including computer vision, speech recognition, natural language processing, etc. In the meantime, deep neural networks based recommender systems have demonstrated impressive abilities in performance improvements, and have led to breakthroughs in some largely underexplored tasks. Examples are recommender systems with integrated multimodal/unstructured data and temporal dynamics. This chapter provides an overview of deep neural networks based recommender systems, with two aims. One is to explain how deep neural networks can be applied to recommendation tasks and the other is to review the recent progress in this field. Specifically, we begin with basic concepts and terminologies about deep neural networks and how they are applied to recommender systems. We then present an overview of the state-of-the-art deep learning based recommendation algorithms, and discuss their strengths and limitations. Finally, we provide an outlook on the future trends and directions which might lead to the next generation of recommender systems.

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Notes

  1. 1.

    In this section, we mainly refer to categorical features.

  2. 2.

    Since there are a large body of related work on news recommendation, we refer readers to Sect. 4 for more details.

  3. 3.

    https://www.flickr.com/.

  4. 4.

    https://www.amazon.com/.

  5. 5.

    https://www.ebay.com/.

  6. 6.

    https://www.alibaba.com/.

  7. 7.

    Note that there is no guarantee that the listed methods are currently in use.

  8. 8.

    https://www.kaola.com/.

  9. 9.

    https://www.pinterest.com/.

  10. 10.

    https://www.yelp.com/.

  11. 11.

    https://foursquare.com/.

  12. 12.

    https://www.google.com/maps.

  13. 13.

    http://www.meituan.com/.

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Authors and Affiliations

  1. ETH Zurich, Zurich, Switzerland

    Shuai Zhang & Ce Zhang

  2. Google, Mountain View, CA, USA

    Yi Tay

  3. UNSW Sydney, Sydney, NSW, Australia

    Lina Yao

  4. NTU, Singapore, Singapore

    Aixin Sun

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  1. Shuai Zhang
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  5. Ce Zhang
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  1. Faculty of Computer Science, Free University of Bozen-Bolzano, Bozen-Bolzano, Italy

    Francesco Ricci

  2. Software and Information Systems Engineering, Ben-Gurion University of the Negev, Beer-Sheva, Israel

    Lior Rokach

  3. Software and Information Systems Engineering, Ben-Gurion University of the Negev, Beer-Sheva, Israel

    Bracha Shapira

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Zhang, S., Tay, Y., Yao, L., Sun, A., Zhang, C. (2022). Deep Learning for Recommender Systems. In: Ricci, F., Rokach, L., Shapira, B. (eds) Recommender Systems Handbook. Springer, New York, NY. https://doi.org/10.1007/978-1-0716-2197-4_5

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