Beyond Factual Correctness: Mitigating Preference-Inconsistent Explanations in Explainable Recommendation

ArXi:2603.03080v2 Announce Type: replace LLM-based explainable recommenders can produce fluent explanations that are factually correct, yet still justify items using attributes that conflict with a user's historical preferences. Such preference-inconsistent explanations yield logically valid but unconvincing reasoning and are largely missed by standard hallucination or faithfulness metrics. We formalize this failure mode and propose PURE, a preference-aware reasoning framework following a select-then-generate paradigm.