CoFeel: Using Emotions for Social Interaction in Group Recommender Systems

Group and social recommender systems aim to suggest items of interest to a group or a community of people. One important issue in such environment is to understand each individual’s preference and attitude within the group. Social and behavioral scientist have evidenced the role of emotions in group work and social communication. This paper aims to examine the role of emotion for social interaction in group recommenders. We implemented CoFeel, an interface that aims to provide emotional input in group recommenders. We further apply CoFeel in a GroupFun, a mobile group music recommender system. Results of an in-depth field study show that by exchanging feelings with other users, CoFeel motivates users to provide feedback on recommended items in a natural and enjoyable way. Results also show that emotions do serve as an effective and promising element to elicitate users’ attitudes, and that they do have the potential to increase user engagement in a group. Based on suggestions collected from users, we propose other potential recommendation domains of CoFeel.

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