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Information foraging for enhancing implicit feedback in content-based image recommendation

  • Amit Kumar Jaiswal
    ,
  • Haiming Liu
    ,
  • Ingo Frommholz
Research Output: Chapter in Book/Report/Conference proceeding Conference contribution Peer-review

Open access

Abstract

User implicit feedback plays an important role in recommender systems. However, finding implicit features is a tedious task. This paper aims to identify users' preferences through implicit behavioural signals for image recommendation based on the Information Scent Model of Information Foraging Theory. In the first part, we hypothesise that the users' perception is improved with visual cues in the images as behavioural signals that provide users' information scent during information seeking. We designed a content-based image recommendation system to explore which image attributes (i.e., visual cues or bookmarks) help users find their desired image. We found that users prefer recommendations predicated by visual cues and therefore consider the visual cues as good information scent for their information seeking. In the second part, we investigated if visual cues in the images together with the images itself can be better perceived by the users than each of them on its own. We evaluated the information scent artifacts in image recommendation on the Pinterest image collection and the WikiArt dataset. We find our proposed image recommendation system supports the implicit signals through Information Foraging explanation of the information scent model.

Publication Information

Output type

Research Output: Chapter in Book/Report/Conference proceeding Conference contribution Peer-review

Original language

English

Pages from-to (Number of pages)

Pages 65-69

Publication milestones

  • Published - 31/12/2019

Publication status

Published - 31/12/2019

Publisher

Association for Computing Machinery, United States

External Publication IDs

  • handle.net: 10547/624157
  • Scopus: 85077513724

Host publication title

FIRE '19: Proceedings of the 11th Annual Meeting of the Forum for Information Retrieval Evaluation

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