让大模型生成更符合用户行为序列的推荐解释。
Sequence-aware Large Language Models for Explainable Recommendation
- 双路编码器融合用户行为与物品语义，增强解释相关性。
- 在多个数据集上解释质量与推荐效果均优于现有方法。
- 兼顾文本质量和实际推荐效果，适合需要可信推荐的场景。
大语言模型（LLMs）在生成推荐系统自然语言解释方面展现出强大潜力。然而，现有方法常忽略用户行为的序列特性，且评估指标与实际应用价值不匹配。本文提出SELLER（基于序列感知的LLM可解释推荐框架），将解释生成与效用感知评估相结合。SELLER采用双路编码器，分别捕捉用户行为序列和物品语义信息，并通过专家混合适配器对齐两者信号与大语言模型。引入统一评估框架，从文本质量与推荐结果影响两方面综合评估解释效果。在多个公开基准数据集上的实验表明，SELLER在解释质量与真实场景效用上均持续优于现有方法。
原文摘要 · Abstract (English)
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.
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