FEBR: Expert-Based Recommendation Framework for Beneficial and Personalized Content

Abstract

So far, most research on recommender systems focused on maintaining long-term user engagement and satisfaction, by promoting relevant and personalized content. However, it is still very challenging to evaluate the quality and the reliability of this content. In this paper, we propose FEBR (Expert-Based Recommendation Framework), a collaborative recommendation framework based on apprenticeship learning to assess the quality of the recommended content on online platforms. FEBR exploits the demonstrated trajectories of a set of trusted users (chosen to be experts with reliable behavior) in a recommendation evaluation environment, to recover an unknown utility function. This function is used to learn an optimal policy describing the experts’ behavior, which is then used in the framework to optimize a user-expert-based recommendation policy with an adapted Q-learning algorithm, providing high-quality and personalized recommendations. We evaluate the performance of our solution through a user interest simulation environment (using RecSim), and compare its efficiency with standard recommendation methods. The results show that our approach provides a significant gain in terms of content quality (evaluated by experts and watched by users) while maintaining an important engagement rate.