Effects of Foraging in Personalized Content-based Image Recommendation
This addresses the challenge of improving user engagement in content-based image recommender systems, though it appears incremental by adapting an existing theory to a specific domain.
The study tackled the problem of understanding user attention in personalized image recommendation by applying Information Foraging Theory to reinforce visual cues, resulting in a stronger scent in the recommended collection as evaluated on the Pinterest dataset.
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.