Cristin-resultat-ID: 1956967
Sist endret: 13. desember 2021, 12:17
NVI-rapporteringsår: 2021
Vitenskapelig Kapittel/Artikkel/Konferanseartikkel

Recommending Videos in Cold Start With Automatic Visual Tags

  • Mehdi Elahi
  • Farshad Bakhshandegan Moghaddam
  • Reza Hosseini
  • Mohammad Hossein Rimaz
  • Nabil El Ioini
  • Marko Tkalcic
  • mfl.


UMAP '21: Adjunct Proceedings of the 29th ACM Conference on User Modeling, Adaptation and Personalization
  • 978-1-4503-8367-7


Association for Computing Machinery (ACM)
NVI-nivå 1

Om resultatet

Vitenskapelig Kapittel/Artikkel/Konferanseartikkel
Publiseringsår: 2021
  • 978-1-4503-8367-7


Fagfelt (NPI)

Fagfelt: Informatikk og datateknikk
- Fagområde: Realfag og teknologi

Beskrivelse Beskrivelse


Recommending Videos in Cold Start With Automatic Visual Tags


This paper addresses the so-called New Item problem in video Recommender Systems, as part of Cold Start. New item problem occurs when a new item is added to the system catalog, and the recommender system has no or little data describing that item. This could cause the system to fail to meaningfully recommend the new item to the users. We propose a novel technique that can generate cold start recommendation by utilizing automatic visual tags, i.e., tags that are automatically annotated by deeply analyzing the content of the videos and detecting faces, objects, and even celebrities within the videos. The automatic visual tags do not need any human involvement and have been shown to be very effective in representing the video content. In order to evaluate our proposed technique, we have performed a set of experiments using a large dataset of videos. The results have shown that the automatically extracted visual tags can be incorporated into the cold start recommendation process and achieve superior results compared to the recommendation based on human-annotated tags.


Aktiv cristin-person

Mehdi Elahi

  • Tilknyttet:
    ved Inst. for informasjon og medievitenskap ved Universitetet i Bergen

Farshad Bakhshandegan Moghaddam

  • Tilknyttet:
    ved Rheinische Friedrich-Wilhelms-Universität Bonn

Reza Hosseini

  • Tilknyttet:
    ved Tyskland

Mohammad Hossein Rimaz

  • Tilknyttet:
    ved Universität Passau

Nabil El Ioini

  • Tilknyttet:
    ved Libera Università di Bolzano
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UMAP '21: Adjunct Proceedings of the 29th ACM Conference on User Modeling, Adaptation and Personalization.

Masthoff, Judith; Herder, Eelco. 2021, Association for Computing Machinery (ACM). UUVitenskapelig antologi/Konferanseserie
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