Improving Recommendation by Exchanging Meta-Information

Interest-based recommendation (IBR) is a kind of knowledge based automated recommendation, in which agents exchange (meta-) information about their underlying goals using argumentation. This helps in improving the quantitative and qualitative utility of a recommendation. IBR combines hybrid recommender system with automated argumentation between agents. IBR also improves recommendation repair activity by discovering interesting alternatives based on user's underlying mental attitude. This paper analyzes the role of interaction between agent's goals to improve recommendation. We give an experimental analysis to show that with increase in knowledge transfer, the benefits of an interest-based recommendation also increase as compared to other recommendation technique without argumentation.