Introducing Affective Agents in Recommendation Systems Based on Relational Data Clustering
نویسندگان
چکیده
This paper proposes the use of a multi-agent system (MAS) with affective agents in a recommendation system based on relational data clustering. This MAS works as a mediator between the user and the data stored in the system. In the proposed system, after logging in, each user will have an affective agent, called Interface agent, for interaction purposes. This agent models the user’s data requests according to the user’s profile (through a relational clustering algorithm) and its affective status, sending it to the Recommender agent, which recommends a set of map points to be visualized. The system analyzes the user’s feedback in order to verify whether the recommended information was satisfactory. This feedback is analyzed through the monitoring of the interaction interface (interaction of a user with the system).
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