IDEAS home Printed from https://ideas.repec.org/a/eee/ininma/v36y2016i3p397-402.html
   My bibliography  Save this article

Collaborative filtering with facial expressions for online video recommendation

Author

Listed:
  • Choi, Il Young
  • Oh, Myung Geun
  • Kim, Jae Kyeong
  • Ryu, Young U.

Abstract

Online video recommender systems help users find videos suitable for their preferences. However, they have difficulty in identifying dynamic user preferences. In this study, we propose a new recommendation procedure using changes of users’ facial expressions captured every moment. Facial expressions portray the users’ actual emotions about videos. We can utilize them to discover dynamic user preferences. Further, because the proposed procedure does not rely on historical rating or purchase records, it properly addresses the new user problem, that is, the difficulty in recommending products to users whose past rating or purchase records are not available. To validate the recommendation procedure, we conducted experiments with footwear commercial videos. Experiment results show that the proposed procedure outperforms benchmark systems including a random recommendation, an average rating approach, and a typical collaborative filtering approach for recommendation to both new and existing users. From the results, we conclude that facial expressions are a viable element in recommendation.

Suggested Citation

  • Choi, Il Young & Oh, Myung Geun & Kim, Jae Kyeong & Ryu, Young U., 2016. "Collaborative filtering with facial expressions for online video recommendation," International Journal of Information Management, Elsevier, vol. 36(3), pages 397-402.
    • Handle: RePEc:eee:ininma:v:36:y:2016:i:3:p:397-402
    DOI: 10.1016/j.ijinfomgt.2016.01.005
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0268401215300220
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.ijinfomgt.2016.01.005?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Jin, Ying & Su, Meng, 2009. "Recommendation and repurchase intention thresholds: A joint heterogeneity response estimation," International Journal of Research in Marketing, Elsevier, vol. 26(3), pages 245-255.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Pomirleanu, Nadia & Chennamaneni, Pavan Rao & Krishen, Anjala S., 2016. "Easy to please or hard to impress: Elucidating consumers' innate satisfaction," Journal of Business Research, Elsevier, vol. 69(5), pages 1914-1918.
    2. Ioana-Mădălina Purcaru & Ana-Maria Urdea & Cristinel Petrişor Constantin & Gabriel Brătucu, 2022. "Building Long-Term Business Sustainability: The Influence of Experiential Marketing on Sales Representatives’ Loyalty to Multi-Level Marketing Systems," Sustainability, MDPI, vol. 14(15), pages 1-16, August.
    3. Jiang, Yuanchun & Shang, Jennifer & Liu, Yezheng & May, Jerrold, 2015. "Redesigning promotion strategy for e-commerce competitiveness through pricing and recommendation," International Journal of Production Economics, Elsevier, vol. 167(C), pages 257-270.
    4. Ravula, Prashanth & Jha, Subhash & Biswas, Abhijit, 2022. "Relative persuasiveness of repurchase intentions versus recommendations in online reviews," Journal of Retailing, Elsevier, vol. 98(4), pages 724-740.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:ininma:v:36:y:2016:i:3:p:397-402. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: https://www.journals.elsevier.com/international-journal-of-information-management .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.