Can Explanations Improve Recommendations? A Joint Optimization with LLM Reasoning

Modern recommender systems rely on large-scale ML models that are data-hungry and black-box. Recent advances in LLMs suggest that explicit reasoning can improve learning efficiency, yet it remains unclear how generative LLMs can systematically improve recommendation tasks that are discriminative in nature. Moreover, in personalized settings, LLMs tend to hallucinate. Existing explainable recommender systems either generate explanations independently of predictions or provide post-hoc rationales; in both cases, explanations do not improve accuracy over black-box recommenders. We argue that when properly calibrated to prediction outcomes, natural-language explanations can in fact improve recommendations. We propose RecPIE (Recommendation with Prediction-Informed Explanations), a framework that jointly optimizes prediction-informed explanations and explanation-informed predictions. In RecPIE, the recommendation task guides the learning of consumer representations, which are used by a trainable LLM to generate explanations for why a consumer may or may not like a product; these explanations are then fed back into a neural recommender to improve predictions. The two components are trained alternately, allowing explanations to be progressively refined based on how much they improve recommendation accuracy. Empirically, on next point-of-interest recommendation using Google Maps data, RecPIE improves accuracy by 3-4% over state-of-the-art baselines and matches the best baseline using only 12% of the training data. Human evaluations show that RecPIE's explanations are preferred 61.5% of the time among five competing methods. To our knowledge, this work is among the first to demonstrate that generative explanation and discriminative recommendation tasks can be jointly learned to outperform standalone approaches on either task.