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- Title
Using affective parameters in a content-based recommender system for images.
- Authors
Tkalčič, Marko; Burnik, Urban; Košir, Andrej
- Abstract
There is an increasing amount of multimedia content available to end users. Recommender systems help these end users by selecting a small but relevant subset of items for each user based on her/his preferences. This paper investigates the influence of affective metadata (metadata that describe the user's emotions) on the performance of a content-based recommender (CBR) system for images. The underlying assumption is that affective parameters are more closely related to the user's experience than generic metadata (e.g. genre) and are thus more suitable for separating the relevant items from the non-relevant. We propose a novel affective modeling approach based on users' emotive responses. We performed a user-interaction session and compared the performance of the recommender system with affective versus generic metadata. The results of the statistical analysis showed that the proposed affective parameters yield a significant improvement in the performance of the recommender system.
- Subjects
METADATA; END users (Information technology); INTERNET content; RECOMMENDER systems; COMPUTER users; DOCUMENT type definitions
- Publication
User Modeling & User-Adapted Interaction, 2010, Vol 20, Issue 4, p279
- ISSN
0924-1868
- Publication type
Article
- DOI
10.1007/s11257-010-9079-z