Explainable Recommender Systems Evaluation

Adding explanations to recommender systems is said to have multiple benefits, such as increasing user trust or system transparency. However, the evaluation of explanations is still an open research topic with no commonly agreed on guidelines or standards.

This project aims to move toward an evaluation approach that allows a researcher to compare the strengths and weaknesses of multiple explanation approaches for recommender systems and investigate their tradeoffs.