用用户-商品图谱评估个性化评论生成的忠实度与一致性。
PeReGrINE: Evaluating Personalized Review Fidelity with User Item Graph Context
- 构建时序一致的用户-商品二分图，用图结构证据生成评论。
- 引入用户风格参数，提升对用户语言习惯的建模能力。
- 发现图像辅助可提升文本质量，但图谱证据仍是核心驱动力。
我们提出PeReGrINE，一个基于图结构用户-商品证据的个性化评论生成评估框架。该框架将Amazon Reviews 2023重构为时序一致的二分图，每个目标评论均基于用户历史、商品上下文及邻近互动，在明确的时间截断下进行条件化。为避免直接依赖稀疏原始历史，我们计算了表征用户长期语言与情感倾向的用户风格参数。该设置支持对四种图衍生检索方式（仅商品、仅用户、仅邻近、联合）的可控对比。除标准生成指标外，引入音调不一致分析（Dissonance Analysis），从宏观层面衡量评论偏离预期用户风格与产品共识的程度。同时研究视觉证据作为辅助上下文来源，发现其在某些场景中可提升文本质量，但图谱证据仍是个性化与一致性的主要驱动因素。该框架覆盖多个商品类别，为探究证据组合如何影响评论忠实度、个性化与生成模型的上下文依附性提供了可复现的路径。
原文摘要 · Abstract (English)
We introduce PeReGrINE, a benchmark and evaluation framework for personalized review generation grounded in graph-structured user--item evidence. PeReGrINE restructures Amazon Reviews 2023 into a temporally consistent bipartite graph, where each target review is conditioned on bounded evidence from user history, item context, and neighborhood interactions under explicit temporal cutoffs. To represent persistent user preferences without conditioning directly on sparse raw histories, we compute a User Style Parameter that summarizes each user's linguistic and affective tendencies over prior reviews. This setup supports controlled comparison of four graph-derived retrieval settings: product-only, user-only, neighbor-only, and combined evidence. Beyond standard generation metrics, we introduce Dissonance Analysis, a macro-level evaluation framework that measures deviation from expected user style and product-level consensus. We also study visual evidence as an auxiliary context source and find that it can improve textual quality in some settings, while graph-derived evidence remains the main driver of personalization and consistency. Across product categories, PeReGrINE offers a reproducible way to study how evidence composition affects review fidelity, personalization, and grounding in retrieval-conditioned language models.
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