The Utility of LLMs in Recommender Systems Explanation Evaluation
Explanations play a crucial role in creating trustworthy recommender systems (RS), yet choosing a good explanation method presents challenges. Many explanation methods exist, but little guidance exists on which is best for which setting. Existing explanation generation methods often produce abstract outputs that require further formatting to become user-friendly, with a seemingly endless pool of options. Running user-based evaluations of all possible options is usually unfeasible, while automated evaluation metrics often either assess only the explainer's abstract output or require comparison with a ground truth, which is generally unavailable. Recent studies have shown that large languag...
| Authors | Kathrin Wardatzky, Oana Inel, Luca Rossetto et al. |
|---|---|
| Primary category | cs.IR |
| Published | 7 August 2026 |
| Last updated at source | 7 August 2026 |
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