Can Offline Metrics Measure Explanation Goals? A Comparative Survey Analysis of Offline Explanation Metrics in Recommender Systems

In Recommender System (RS), explanations help users understand why items are recommended and can enhance a system's transparency, persuasiveness, engagement, and trust, which are known as explanation goals. However, evaluating the effectiveness of explanation algorithms offline remains challenging because explanation goals are inherently subjective. We initially conducted a rapid literature review, which revealed that algorithms are often assessed using anecdotal evidence (offering convincing examples) or using metrics that do not align with human perception. From these results, we investigated whether the selection of item attributes and interacted items affects explanation goals in explanations that generate a path connecting interacted and recommended items based on shared attributes (such as genres). We used metrics that measure the diversity and popularity of attributes and the recency of item interactions to evaluate explanations from three state-of-the-art agnostic algorithms across six recommendation systems. We then performed an online user study to compare user perceptions of explanation goals and offline metrics. Our findings indicate that engagement is sensitive to users' perceptions of diversity in explanations, whereas transparency, trust, and persuasiveness are influenced by perceptions of both popularity and diversity. However, offline metrics require refinement to more closely align with explanation goals and user understanding.