arXiv cs.AI· Leyao Wang, Xutao Mao, Qinwen Ge, Xuhui Zhan, Yuying Zhao, Bo Ni, Ryan A. Rossi, Nesreen K. Ahmed, Tyler Derr·· 11 小时前AI 评分24
TWISTER：面向稀疏评论场景的结构感知评论补全推荐框架
When Reviews Are Scarce: Structure-Aware Review Imputation for Recommendation in Realistic Sparse Review Settings
AI 导读
研究者提出 TWISTER（ToWards Imputation on Sparsity with Textual Edge Graph Representation），通过将用户-物品交互建模为以评论为边属性的 Textual-Edge Graph，并用 LLM 作为图感知聚合器，为缺少评论的交互生成个性化评论文本。
正文 · 原文
Abstract:Textual reviews enrich recommender systems with fine-grained preference signals and enhanced explainability. However, in real-world scenarios, users rarely leave reviews, resulting in severe sparsity that undermines the effectiveness of review-aware recommenders under partial observability. A natural remedy is to synthesize the missing reviews to restore the textual signal these recommenders rely on. Yet, conventional imputation techniques, such as matrix completion and LLM-based augmentation, either lose contextualized semantics by embedding texts into vectors or overlook structural dependencies on the user-item graph. To address these shortcomings, we propose TWISTER (ToWards Imputation on Sparsity with Textual Edge Graph Representation), a unified framework for sparse-text recommendation that synthesizes missing reviews by jointly modeling semantic and structural signals. Specifically, we represent user-item interactions as a Textual-Edge Graph (TEG), treating reviews as edge attributes. To capture relational context, we construct line-graph views and employ an LLM as a graph-aware aggregator. For each interaction lacking a textual review, our model aggregates natural-language representations from neighboring interactions to generate a coherent and personalized review. Experiments on multiple datasets show that TWISTER consistently outperforms traditional numeric, graph-based, and LLM baselines, delivering higher-quality imputed reviews and, more importantly, improved downstream recommendation. In summary, TWISTER is a drop-in, structure-aware imputer that recovers the textual signal that review-aware recommenders need when reviews are scarce.
| Subjects: | Information Retrieval (cs.IR); Artificial Intelligence (cs.AI); Machine Learning (cs.LG) |
| Cite as: | arXiv:2508.01128 [cs.IR] |
| (or arXiv:2508.01128v2 [cs.IR] for this version) | |
| https://doi.org/10.48550/arXiv.2508.01128 arXiv-issued DOI via DataCite |
Submission history
From: Leyao Wang [view email]
[v1]
Sat, 2 Aug 2025 00:53:40 UTC (3,628 KB)
[v2]
Tue, 3 Nov 2026 20:19:23 UTC (3,066 KB)
来源：arXiv cs.AI · arxiv.org