Exploiting implicit affective labeling for image recommendations
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Recent work has shown an increase of accuracy in recommender systems that use affective labels. In this paper we compare three labeling methods within a recommender system for images: (i) generic labeling, (ii) explicit affective labeling and (iii) implicit affective labeling. The results show that the recommender system performs best when explicit labels are used. However, implicitly acquired labels yield a significantly better performance of the CBR than generic metadata while being an unobtrusive feedback tool. © 2012 IEEE.