Abstract
Recommender systems have been successfully applied to alleviate the information overload and assist user's decision makings. Emotional states have been demonstrated as effective factors in recommender systems. However, how to collect or predict a user's emotional state becomes one of the challenges to build affective recommender systems. In this paper, we explore and compare different solutions to predict emotions to be applied in the recommendation process. More specifically, we propose an approach named as collaborative chains. It predicts emotional states in a collaborative way and additionally takes correlations among emotions into consideration. Our experimental results based on a movie rating data demonstrate the effectiveness of affective prediction by collaborative chains in movie recommendations.
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