这篇论文告诉你：别盲目堆文本特征做推荐，协同过滤本身已经很强了，加文本未必有多大提升。
该论文系统评估了文本信息对矩阵分解推荐模型的实际贡献。作者提出了可学习的门控机制和交叉注意力机制，用于融合用户/物品主题分布和全文嵌入两种文本表征。在多个基于评论的数据集上测试了六种变体，发现即使自适应融合机制提升了表征灵活性，相对于协同过滤主干，文本信号的边际贡献仍有限。这表明在典型评分预测场景下，协同信息仍主导性能。
How Much Do Reviews Really Contribute? A Study on Text-Enriched Matrix Factorization for Recommendations
Incorporating textual reviews into a Recommender System has become a prominent strategy for enriching collaborative signals with semantic information. However, the actual contribution of review-derived representations remains an open question, particularly when strong collaborative baselines are employed. In this work, we systematically investigate the impact of textual information on Matrix Factorization by introducing and comparing three enrichment strategies over a common collaborative backbone. First, we propose a learnable gating mechanism that adaptively balances collaborative and textual signals during training. This mechanism is applied to two distinct review representations: (i) aggregated topic profiles extracted from user and item histories, and (ii) full text embedding representations derived from reviews. Additionally, we explore a cross-attention mechanism that identifies and emphasizes the most informative dimensions of the textual representation before fusion with collaborative factors. We evaluate six variants: pure, enriched with topic profiles and text via gating; enriched with topics and text via gating; and enhanced with cross-attention over textual features. Experiments across multiple review-based datasets reveal that although adaptive fusion mechanisms improve representation flexibility, the marginal contribution of textual signals remains limited compared to the collaborative backbone. These findings suggest that, under typical rating-prediction settings, collaborative information continues to dominate performance, raising important considerations for the effective integration of semantic review signals into recommendation models.