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.

翻译：将文本评论融入推荐系统已成为一种通过语义信息丰富协同信号的主流策略。然而，当采用强大的协同基线模型时，评论衍生表示的实际贡献仍是一个有待解决的问题。本研究通过在统一的协同骨干网络上引入并比较三种增强策略，系统性地探究了文本信息对矩阵分解的影响。首先，我们提出一种可学习的门控机制，在训练过程中自适应地平衡协同信号与文本信号。该机制应用于两种不同的评论表示：（i）从用户和项目历史中提取的聚合主题轮廓，以及（ii）基于评论的全文本嵌入表示。此外，我们探索了一种交叉注意力机制，在文本表示与协同因子融合前识别并强调其最具信息量的维度。我们评估了六种变体：纯协同模型、通过门控机制增强主题轮廓或全文本的模型、同时增强主题与文本的模型，以及引入文本特征交叉注意力的增强模型。在多个基于评论的数据集上的实验表明：尽管自适应融合机制提升了表示灵活性，但文本信号的边际贡献相较于协同骨干网络仍十分有限。这些发现意味着，在典型的评分预测场景下，协同信息仍主导推荐性能，这为在推荐模型中有机融合语义评论信号提出了重要思考。