Enhancing Explainable Recommendation with Semantic Decomposition and Reconstruction

Generating natural-language explanations is an important task in explainable recommendation, as it helps users understand why an item is recommended. However, existing generative methods typically encode user, item, and recommendation signals as entangled conditioning inputs, leaving explanation-relevant structure to be inferred implicitly by the language model. This leads to suboptimal explanation generation performance.In this paper, we propose a structured explanation generation framework for explainable recommendation, which explicitly models explanation-relevant information instead of leaving it fully entangled in recommendation signals. Specifically, the framework decomposes explanatory preferences into two complementary components: general explanatory preferences, which capture domain-shared explanatory aspects and their importance weights, and personalized explanatory preferences, which capture residual user- and item-specific explanation signals beyond the shared aspect space. Based on this decomposition, we organize the learned explanatory representations into a structured prompt for explanation generation, and further introduce a regularization mechanism to improve explanation generation performance. Experiments on three benchmark datasets with different language-model backbones demonstrate that the proposed framework consistently improves recommendation explanation generation. The results highlight the benefit of explicitly modeling explanation-relevant structure for generative explainable recommendation.