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Interest-Based personalized Recommender System

The challenge in a recommendation system is to help users in dealing with the problem of information overload. Personalization, when applied to recommendation in e-market can transform a product into a dedicated solution for an individual. In this paper, we describe the method used for personalization of recommendations generated by an Interest-Based Recommender System (IBRS). This paper proposes a design framework for a personalized multi-agent IBRS. The IBRS is an agent- based recommender system that takes into account user's preferences to generate recommendations. The system is based on the agents having Belief-Desire-Intention (BDI) architecture. These BDI user agents are empowered with cognitive capabilities and interact with recommender and other user agents using argumentation. The explanation process uses argumentation so that the recommender can look deeper into the reasons behind user's likes and dislikes. The IBRS considers user's feedback for recommendation repair action. This results in improvement of the personalization process. The experimental study is conducted for Travel Recommender System to show that personalized interest-based recommendations improve quality.