PeReGrINE: Evaluating Personalized Review Fidelity with User Item Graph Context

TL;DR

PeReGrINE is a comprehensive framework for evaluating personalized review generation using graph-structured evidence, enabling controlled studies of evidence impact on review fidelity and personalization.

Contribution

It introduces a new benchmark, evaluation methods, and evidence integration techniques for personalized review generation grounded in user-item graph context.

Findings

Graph-derived evidence enhances review personalization.

Visual evidence can improve textual quality in some cases.

The framework supports reproducible studies across product categories.

Abstract

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.