Recommender systems have become an integral component of the digital landscape, impacting a multitude of services and industries ranging from e-commerce to entertainment and beyond. By offering personalised suggestions, these systems challenge a fundamental problem in our modern information society named information overload. As users face a deluge of choices, recommender systems help sift through this immense sea of possibilities, delivering a personalised subset of options that align with user preferences and historical behaviour.

However, despite their considerable utility, recommender systems often operate as “black boxes,” obscuring the rationale behind recommendations. This opacity can engender mistrust and undermine user engagement, thus attenuating the overall effectiveness of the system. Researchers have emphasized the importance of explanations in recommender systems, highlighting how explanations can enhance system transparency, foster user trust, and improve decision-making processes, thereby enriching user experiences and yielding potential business benefits. Yet, a significant gap persists in the current state of human-understandable explanations research. While recommender systems have grown increasingly complex, our capacity to generate clear, concise, and relevant explanations that reflect this complexity remains limited. Crafting explanations that are both understandable and reflective of sophisticated algorithmic decision-making processes poses a significant challenge, especially in a manner that meets the user’s cognitive and contextual needs.

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