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A Hybrid Recommendation System Based on Bidirectional Encoder Representations

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ECML PKDD 2020 Workshops (ECML PKDD 2020)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1323))

Abstract

Using auxiliary data about items provides more accurate item recommendations when utilizing deep learning in the recommendation system. Users often read item descriptions during online shopping, which contain key information about the item and its features. However the item descriptions are in unstructured form and using them in the deep learning model is a problem. In this study, we integrate a pioneering Natural Language Processing technique into a recommendation system to create an item embedding vector from unstructured item description text. The experimental results show that the proposed approach is efficient in generating more accurate recommendations by creating item embedding vectors from unstructured item description text.

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Notes

  1. 1.

    http://jmcauley.ucsd.edu/data/amazon/.

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Author information

Authors and Affiliations

  1. Department of Computer Engineering, ITU AI Research and Application Center, Istanbul Technical University, Istanbul, Turkey

    Irem Islek & Sule Gunduz Oguducu

Authors
  1. Irem Islek
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  2. Sule Gunduz Oguducu
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Corresponding author

Correspondence to Irem Islek .

Editor information

Editors and Affiliations

  1. University of Sydney, Sydney, NSW, Australia

    Irena Koprinska

  2. Monash University, Clayton, VIC, Australia

    Michael Kamp

  3. University of Bari Aldo Moro, Bari, Italy

    Annalisa Appice

  4. University of Bari Aldo Moro, Bari, Italy

    Corrado Loglisci

  5. University of Guelph, Guelph, ON, Canada

    Luiza Antonie

  6. University of Caen Normandy, Caen, France

    Albrecht Zimmermann

  7. University of Pisa, Pisa, Italy

    Riccardo Guidotti

  8. Norwegian University of Science and Technology, Trondheim, Norway

    Özlem Özgöbek

  9. University of Porto, Porto, Portugal

    Rita P. Ribeiro

  10. UPC BarcelonaTech, Barcelona, Spain

    Ricard Gavaldà

  11. University of Porto, Porto, Portugal

    João Gama

  12. Fraunhofer IAIS, St. Augustin, Germany

    Linara Adilova

  13. Royal Holloway University of London, Egham, UK

    Yamuna Krishnamurthy

  14. University of Lisbon, Lisbon, Portugal

    Pedro M. Ferreira

  15. University of Bari Aldo Moro, Bari, Italy

    Donato Malerba

  16. University of Lisbon, Lisbon, Portugal

    Ibéria Medeiros

  17. University of Bari Aldo Moro, Bari, Italy

    Michelangelo Ceci

  18. ICAR-CNR, Rende, Italy

    Giuseppe Manco

  19. University of Naples Federico II, Naples, Italy

    Elio Masciari

  20. University of North Carolina, Charlotte, NC, USA

    Zbigniew W. Ras

  21. Australian National University, Canberra, ACT, Australia

    Peter Christen

  22. Leibniz University Hannover, Hannover, Germany

    Eirini Ntoutsi

  23. Technical University of Dortmund, Dortmund, Germany

    Erich Schubert

  24. University of Southern Denmark, Odense, Denmark

    Arthur Zimek

  25. University of Pisa, Pisa, Italy

    Anna Monreale

  26. Warsaw University of Technology, Warsaw, Poland

    Przemyslaw Biecek

  27. ISTI-CNR, PISA, Italy

    Salvatore Rinzivillo

  28. Berlin Institute of Technology, Berlin, Germany

    Benjamin Kille

  29. Berlin Institute of Technology, Berlin, Germany

    Andreas Lommatzsch

  30. Norwegian University of Science and Technology, Trondheim, Norway

    Jon Atle Gulla

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Islek, I., Oguducu, S.G. (2020). A Hybrid Recommendation System Based on Bidirectional Encoder Representations. In: Koprinska, I., et al. ECML PKDD 2020 Workshops. ECML PKDD 2020. Communications in Computer and Information Science, vol 1323. Springer, Cham. https://doi.org/10.1007/978-3-030-65965-3_14

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  • DOI: https://doi.org/10.1007/978-3-030-65965-3_14

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-65964-6

  • Online ISBN: 978-3-030-65965-3

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