  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), an apprenticeship learning framework to assess the
quality of the recommended content on online platforms. The framework exploits
the demonstrated trajectories of an expert (assumed to be reliable) in a
recommendation evaluation environment, to recover an unknown utility function.
This function is used to learn an optimal policy describing the expert's
behavior, which is then used in the framework to provide high-quality and
personalized recommendations. We evaluate the performance of our solution
through a user interest simulation environment (using RecSim). We simulate
interactions under the aforementioned expert policy for videos recommendation,
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 almost the same
watch time as the baseline approaches.
