Trust enabled Argumentation Based Recommender System

The goal of Recommender Systems (RSs) is to help users to deal with the problem of information overload by facilitating access to relevant items that are valuable to them. If the recommended items match the user preferences, user trust in the system increases and the user start liking the system and uses it more frequently. Trust enabled Argumentation Based Recommender System (TABRS) designed and developed in this paper recommends items of interest to the user by using a hybrid approach for recommendation. These recommendations are further improved using argumentation to convince users about the product. TABRS is an agent-based recommender system that takes into account user's changing preferences to generate interesting recommendations. TABRS combines hybrid recommender system with automated argumentation between agents. The system also improves recommendation repair activity by discovering interesting alternatives based on user's underlying mental attitudes. We implemented the system using Jason for building agents enabled with inference and interaction capabilities. The experimental study is conducted for a Book Recommender System and performance of the proposed system is evaluated using precision and recall metrics.