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Explanations in a Recommender System (RS) provide reasons for recommendationsto users and can enhance transparency, persuasiveness, engagement, andtrust-known as explanation goals. Evaluating the effectiveness of explanationalgorithms offline remains challenging due to subjectivity. Initially, weconducted a literature review on current offline metrics, revealing thatalgorithms are often assessed with anecdotal evidence, offering convincingexamples, or with metrics that don't align with human perception. Weinvestigated whether, in explanations connecting interacted and recommendeditems based on shared content, the selection of item attributes and interacteditems affects explanation goals. Metrics measuring the diversity and popularityof attributes and the recency of item interactions were used to evaluateexplanations from three state-of-the-art agnostic algorithms across sixrecommendation systems. These offline metrics were compared with results froman online user study. Our findings reveal a trade-off: transparency and trustrelate to popular properties, while engagement and persuasiveness are linked todiversified properties. This study contributes to the development of morerobust evaluation methods for explanation algorithms in recommender systems.

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