Skip to search boxSkip to navigationSkip to main content

Effects of foraging in personalized content-based image recommendation

  • Haiming Liu
    ,
  • Amit Kumar Jaiswal
    ,
  • Ingo Frommholz
Research Output:
Other contribution
Other contribution
 Peer-review

Open access

Abstract

A major challenge of recommender systems is to help users locating interesting items. Personalized recommender systems have become very popular as they attempt to predetermine the needs of users and provide them with recommendations to personalize their navigation. However, few studies have addressed the question of what drives the users' attention to specific content within the collection and what influences the selection of interesting items. To this end, we employ the lens of Information Foraging Theory (IFT) to image recommendation to demonstrate how the user could utilize visual bookmarks to locate interesting images. We investigate a personalized content-based image recommendation system to understand what affects user attention by reinforcing visual attention cues based on IFT. We further find that visual bookmarks (cues) lead to a stronger scent of the recommended image collection. Our evaluation is based on the Pinterest image collection.

Publication Information

Output type

Research Output:
Other contribution
Other contribution
 Peer-review

Original language

English

Publication milestones

  • Published - 30/06/2019

Publication status

Published - 30/06/2019

Publication IDs

  • handle.net: 10547/623795

Access to documents

Publication metrics

Metrics

Download statistics
Download count
6