Emotion-Based Recommender System for Overcoming the Problem of Information Overload

Nowadays, we are experiencing a huge growth in the available information, caused by the advent of communication technology, which humans cannot handle by themselves. Personal Assistant Agents can help humans to cope with the task of selecting the relevant information. In order to perform well, these agents should consider not only their preferences, but also their mental states (such as beliefs, intentions and emotions) when recommending information. In this paper, we describe an ongoing Recommender System application, that implements a Multiagent System, with the purpose of gathering heterogeneous information from different sources and selectively deliver it based on: user’s preferences; the community’s trends; and on the emotions that it elicits in the user.