Over the years, recommender systems have been systematically applied in both industry and academia to assist users in dealing with information overload. One of the factors that determine the performance of a recommender system is user feedback, which has been traditionally communicated through the application of explicit and implicit feedback techniques. In this paper, we propose a novel video search interface that predicts the topical relevance of a video by analysing affective aspects of user behaviour. We, furthermore, present a method for incorporating such affective features into user profiling, to facilitate the generation of meaningful recommendations, of unseen videos. Our experiment shows that multimodal interaction feature is a promising way to improve the performance of recommendation.
This way, the topical relevance of a recommended video has been predicted by analyzing affective aspects of user behavior REF ).
Integrating facial expressions into user profiling for the improvement of a multimodal recommender system
2009 IEEE International Conference on Multimedia and Expo
2009 IEEE International Conference on Multimedia and Expo
Computer Science