Exploiting implicit affective labeling for image recommendations
Marko Tkalčič, Ante Odić, Andrej Košir, Jurij Franc Tasič · 2012
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.