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Hybrid Recommendation of Movies Based on Deep Content Features

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Service-Oriented Computing – ICSOC 2021 Workshops (ICSOC 2021)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13236))

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Abstract

When a movie is uploaded to a movie Recommender System (e.g., YouTube), the system can exploit various forms of descriptive features (e.g., tags and genre) in order to generate personalized recommendation for users. However, there are situations where the descriptive features are missing or very limited and the system may fail to include such a movie in the recommendation list. This paper investigates hybrid recommendation based on a novel form of content features, extracted from movies, in order to generate recommendation for users. Such features represent the visual aspects of movies, based on Deep Learning models, and hence, do not require any human annotation when extracted. We have evaluated our proposed technique using a large dataset of movies and shown that automatically extracted visual features can mitigate the cold-start problem by generating recommendation with a superior quality compared to different baselines, including recommendation based on human-annotated features.

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Acknowledgements

This work was supported by industry partners and the Research Council of Norway with funding to MediaFutures: Research Centre for Responsible Media Technology and Innovation, through The Centres for Research-based Innovation scheme, project number 309339.

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Authors and Affiliations

  1. MediaFutures: Research Centre for Responsible Media Technology and Innovation, Department of Information Science and Media Studies, University of Bergen, Fosswinckels Gate 6, 5007, Bergen, Norway

    Tord Kvifte, Mehdi Elahi & Christoph Trattner

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  1. Tord Kvifte
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  2. Mehdi Elahi
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  3. Christoph Trattner
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Corresponding author

Correspondence to Tord Kvifte .

Editor information

Editors and Affiliations

  1. Zayed University, Dubai, United Arab Emirates

    Hakim Hacid

  2. Zayed University, Abu Dhabi, United Arab Emirates

    Monther Aldwairi

  3. Deakin University, Waurn Ponds, VIC, Australia

    Mohamed Reda Bouadjenek

  4. National Research Council C.N.R., Pisa, Italy

    Marinella Petrocchi

  5. University of Lyon, Villeurbanne, France

    Noura Faci

  6. Zayed University, Dubai, United Arab Emirates

    Fatma Outay

  7. Macquarie University, Sydney, NSW, Australia

    Amin Beheshti

  8. Technical University of Berlin, Berlin, Germany

    Lauritz Thamsen

  9. Royal Melbourne Institute of Technology, Melbourne, VIC, Australia

    Hai Dong

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Kvifte, T., Elahi, M., Trattner, C. (2022). Hybrid Recommendation of Movies Based on Deep Content Features. In: Hacid, H., et al. Service-Oriented Computing – ICSOC 2021 Workshops. ICSOC 2021. Lecture Notes in Computer Science, vol 13236. Springer, Cham. https://doi.org/10.1007/978-3-031-14135-5_3

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  • DOI: https://doi.org/10.1007/978-3-031-14135-5_3

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