用可控语义提取提升推荐可解释性，减少大模型幻觉干扰。
HADSF: Aspect Aware Semantic Control for Explainable Recommendation
- 分两阶段提取评论中的观点三元组，先构建精炼词表再约束抽取
- 发现幻觉严重度与评分预测误差呈非单调关系，小模型性能显著提升
- 提供新评估指标和开源工具，适合关注可解释推荐的研究者
大语言模型虽有望提升基于评论的推荐系统信息提取效果，但现有方法仍存在（i）缺乏范围控制地挖掘自由文本，导致冗余噪声；（ii）缺少连接模型幻觉与下游效果的合理度量；（iii）未探索不同规模模型在成本-质量间的权衡。本文提出超适应双阶段语义框架（HADSF），先通过自适应选择构建紧凑的跨文档层面方面词汇表，再以该词表引导、显式约束地提取结构化方面-意见三元组。为评估表示保真度，引入方面漂移率（ADR）和意见保真率（OFR），实证发现幻觉严重度与评分预测误差之间存在非单调关系。在涵盖1.5B至70B参数的多个大模型上，对约300万条评论进行实验，结果表明：将HADSF集成到标准评分预测器中，可稳定降低预测误差，并使小型模型在典型部署场景下达到有竞争力的性能。代码、数据流程与度量实现已公开，支持可复现的幻觉感知型、大模型增强的可解释推荐研究。
原文摘要 · Abstract (English)
Recent advances in large language models (LLMs) promise more effective information extraction for review-based recommender systems, yet current methods still (i) mine free-form reviews without scope control, producing redundant and noisy representations, (ii) lack principled metrics that link LLM hallucination to downstream effectiveness, and (iii) leave the cost-quality trade-off across model scales largely unexplored. We address these gaps with the Hyper-Adaptive Dual-Stage Semantic Framework (HADSF), a two-stage approach that first induces a compact, corpus-level aspect vocabulary via adaptive selection and then performs vocabulary-guided, explicitly constrained extraction of structured aspect-opinion triples. To assess the fidelity of the resulting representations, we introduce Aspect Drift Rate (ADR) and Opinion Fidelity Rate (OFR) and empirically uncover a nonmonotonic relationship between hallucination severity and rating prediction error. Experiments on approximately 3 million reviews across LLMs spanning 1.5B-70B parameters show that, when integrated into standard rating predictors, HADSF yields consistent reductions in prediction error and enables smaller models to achieve competitive performance in representative deployment scenarios. We release code, data pipelines, and metric implementations to support reproducible research on hallucination-aware, LLM-enhanced explainable recommendation. Code is available at https://github.com/niez233/HADSF
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