Large Language Models (LLMs) have shown strong potential in generating natural language explanations for recommender systems. However, existing methods often overlook the sequential dynamics of user behavior and rely on evaluation metrics misaligned with practical utility. We propose SELLER (SEquence-aware LLM-based framework for Explainable Recommendation), which integrates explanation generation with utility-aware evaluation. SELLER combines a dual-path encoder-capturing both user behavior and item semantics with a Mixture-of-Experts adapter to align these signals with LLMs. A unified evaluation framework assesses explanations via both textual quality and their effect on recommendation outcomes. Experiments on public benchmarks show that SELLER consistently outperforms prior methods in explanation quality and real-world utility.

翻译：大语言模型在生成推荐系统的自然语言解释方面展现出强大潜力。然而，现有方法常忽视用户行为的序列动态特性，并依赖于与实际效用不匹配的评估指标。我们提出SELLER（面向可解释推荐的序列感知大语言模型框架），该框架将解释生成与效用感知评估相结合。SELLER采用双路径编码器（同时捕捉用户行为与物品语义）及混合专家适配器，将这些信号与大语言模型对齐。统一的评估框架通过文本质量及其对推荐效果的影响双维度衡量解释。在公开基准上的实验表明，SELLER在解释质量与实际效用方面持续优于现有方法。