Enhancing Recommendation Explanations through User-Centric Refinement (2025)

Abstract: Generating natural language explanations for recommendations has become increasingly important in recommender systems. Traditional approaches typically treat user reviews as ground truth for explanations and focus on improving review prediction accuracy by designing various model architectures. However, due to limitations in data scale and model capability, these explanations often fail to meet key user-centric aspects such as factuality, personalization, and sentiment coherence, significantly reducing their overall helpfulness to users.In this paper, we propose a novel paradigm that refines initial explanations generated by existing explainable recommender models during the inference stage to enhance their quality in multiple aspects. Specifically, we introduce a multi-agent collaborative refinement framework based on large language models. To ensure alignment between the refinement process and user demands, we employ a plan-then-refine pattern to perform targeted modifications. To enable continuous improvements, we design a hierarchical reflection mechanism that provides feedback to the refinement process from both strategic and content perspectives. Extensive experiments on three datasets demonstrate the effectiveness of our framework.

## introduction natural language explainable recommendation (nler) aims to generate textual explanations that clarify why an item is recommended (or not), offering high flexibility and user interpretability @xcite . in this field, researchers typically treat user reviews as ground-truth explanations and focus on developing advanced architectures to improve review prediction accuracy. for instance, nrt @xcite incorporates predicted ratings into the explanation generation process. pe-ter @xcite bridges ids and texts using transformer to produce explanations. pepler @xcite leverages prompt learning to further enhance the explanation quality. while leveraging reviews can help explanations partially capture user preference, limitations in data scale and model capability often hinder the overall helpfulness of these explanations, resulting in deficiencies in key user-centric aspects. to effectively support user decision-making, it is essential for explanations to align with user demands on various aspects. for example, factuality ensures that the content is correct and verifiable, personalization requires explanations to highlight specific item features and user characteristics, and coherence demands alignment between the explanation's sentiment and the system-predicted user preference. figure 1 illustrates these shortcomings, underscoring the importance of generating explanations that are both accurate and genuinely helpful. inspired by large reasoning models, which enhance reasoning ability through stepwise thinking and strategic modification during inference, we propose a novel paradigm that refines initial explanations produced by existing explainable recommendation models, yielding targeted improvements across multiple user-centric aspects. analogous to the reranking process in recommender systems @xcite , our paradigm enhances performance in a post-hoc man-ner, which we refer to as refinement. this represents the first attempt in the field of explainable recommendation to modify explanations before presenting them to users. we perform this refinement by employing large language models (llms) with carefully designed instructions. while this idea is promising, it poses several challenges. first, user demands for explanations are often multifaceted, with varying priorities across different aspects @xcite . thus, the refinement process must effectively align with these diverse preferences and enable targeted improvements. second, most existing explainable recommender models @xcite @xcite generate explanations in a single attempt, without assessing whether they satisfy user needs, often resulting in suboptimal outcomes. therefore, it is crucial to incorporate feedback into the refinement process, allowing explanations to evolve iteratively from weak to strong and achieve continuous improvements. to address these challenges, we propose an llm-based multi-agent collaborative framework for explanation refinement. specifically, it adopts a plan-then-refine pattern for targeted modifications guided by user demands, where the planner first identifies which aspect should be refined at each round, and then the refiner modifies the explanations according to the corresponding instructions. additionally, to ensure continuous improvement, we design a hierarchical reflection mechanism, where the reflector provides timely feedback and suggestions by analyzing the refinement process from both strategic and content perspectives. furthermore, to support this process, we maintain an aspect library containing essential information about various aspects. by combining forward refinement and backward reflection phases, our framework achieves self-evolving and iterative enhancement until user demands are satisfied. our key contribution is to enhance recommendation explanation quality across multiple usercentric aspects, which is the first to achieve this. specifically, (1) we identify key limitations of existing explainable recommender models in usercentric aspects and propose a novel paradigm to perform targeted refinement to explanations (called refinex). ## preliminary natural language explainable recommendation (nler) aims to provide textual explanations that clarify why an item is recommended (or not). formally, given a user set u and an item set i, their interactions are recorded in @xmath0, i, r u,i , s u,i |@xmath1, @xmath2, where r u,@xmath3, 5] denotes the user's rating, and s u,i is the corresponding review. given a user-item pair (u, i), nler predicts the rating rui , indicating user preference, and generates a textual explanation e ui to justify the recommendation. since rating prediction has been well studied, this work focuses on explanation generation, where user reviews are commonly treated as ground-truth explanations, and prior work mainly develops advanced architectures to improve review prediction accuracy @xcite @xcite . despite these advances, such approaches often fall short in capturing key user-centric aspects, limiting the practical utility of the explanations. ## conclusion in this paper, we highlight the limitations of existing explainable recommender models in meeting user-centric demands and propose a novel paradigm for targeted explanation refinement. to this end, we design an llm-based multi-agent collaborative framework that adopts a plan-then-refine strategy and incorporates a hierarchical reflection mechanism. extensive experiments demonstrate the effectiveness of our framework in improving user-centric explanation quality and its adaptability to diverse user demands, ultimately enhancing the helpfulness of explanations.