Evaluating collaborative filtering recommender systems
ACM Transactions on Information Systems (TOIS)
Incorporating contextual information in recommender systems using a multidimensional approach
ACM Transactions on Information Systems (TOIS)
IEEE Transactions on Knowledge and Data Engineering
EmoPlayer: A media player for video clips with affective annotations
Interacting with Computers
A Survey of Affect Recognition Methods: Audio, Visual, and Spontaneous Expressions
IEEE Transactions on Pattern Analysis and Machine Intelligence
Emotion-based music recommendation by affinity discovery from film music
Expert Systems with Applications: An International Journal
Exploiting facial expressions for affective video summarisation
Proceedings of the ACM International Conference on Image and Video Retrieval
ICME'09 Proceedings of the 2009 IEEE international conference on Multimedia and Expo
Using affective parameters in a content-based recommender system for images
User Modeling and User-Adapted Interaction
Adaptive extraction of highlights from a sport video based on excitement modeling
IEEE Transactions on Multimedia
Recognizing Human Emotional State From Audiovisual Signals
IEEE Transactions on Multimedia
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Affective labeling of multimedia content can be useful in recommender systems. In this paper we compare the effect of implicit and explicit affective labeling in an image recommender system. The implicit affective labeling method is based on an emotion detection technique that takes as input the video sequences of the users' facial expressions. It extracts Gabor low level features from the video frames and employs a kNN machine learning technique to generate affective labels in the valence-arousal-dominance space. We performed a comparative study of the performance of a content-based recommender (CBR) system for images that uses three types of metadata to model the users and the items: (i) generic metadata, (ii) explicitly acquired affective labels and (iii) implicitly acquired affective labels with the proposed methodology. The results showed that the CBR 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.