Using affective parameters in a content-based recommender system for images
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SubjectValence-arousal-dominance; User profile; Metadata; Item profile; IAPS; Affective modeling; Emotion induction; content-based recommender system; Machine learning
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.
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Tkalcic M; Odic A; Kosir A; Tasic J (IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC, 2013)Affective labeling of multimedia content has proved to be useful in recommender systems. In this paper we present a methodology for the implicit acquisition of affective labels for images. It is based on an emotion detection ...
Deldjoo Y; Elahi M; Quadrana M; Cremonesi P (Springer Verlag, 2015)One of the challenges in video recommendation systems is the New Item problem, which happens when the system is unable to recommend video items, that no information is available about them. For example, in the popular ...