Information foraging for enhancing implicit feedback in content-based image recommendation
Affiliation
University of BedfordshireIssue Date
2019-12-31Subjects
content-based image recommendationinformation foraging
Subject Categories::G500 Information Systems
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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.Citation
Jaiswal AK, Liu H, Frommholz I (2019) 'Information foraging for enhancing implicit feedback in content-based image recommendation', 11th Forum for Information Retrieval Evaluation - Kolkata, ACM.Publisher
ACMType
Conference papers, meetings and proceedingsLanguage
enae974a485f413a2113503eed53cd6c53
10.1145/3368567.3368583
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