Modern recommendation systems can achieve high performance by fusing user behavior graphs (via GNNs) and review texts (via LLMs). However, this fusion faces three significant issues: (1) False Negatives in contrastive learning can degrade the training signal by penalizing similar items; (2) Popularity Bias, often encoded as embedding magnitude, can distort similarity scores; and (3) Signal Ambiguity, which arises from the conflation of objective facts with subjective sentiment in reviews. These interconnected issues can prevent models from learning users' true preferences. In this paper, we propose SymCERE (Symmetric SINCERE), a contrastive learning method that addresses these three issues simultaneously through its structural design. First, we introduce a symmetric application of the SINCERE loss for cross-modal alignment, which is designed to eliminate false negatives in recommendation. Second, by integrating this with L2 normalisation under a "magnitude-as-noise" hypothesis, we aim to mitigate popularity bias by forcing the model to encode preferences primarily in the vector's direction. Experiments on 15 datasets from three distinct platforms (e-commerce, local reviews, and travel) demonstrate that SymCERE outperforms several strong baselines, achieving a relative improvement of up to 43.6% on NDCG@10. Furthermore, a detailed LIME analysis shows that the model learns to anchor alignment on objective, informative vocabulary (e.g., "OEM," "compatible," "gasket"), while placing less emphasis on generic sentiment (e.g., "good," "great"). This suggests that effective semantic alignment stems from understanding factual product attributes, offering a path toward more accurate recommendation systems. The code is available at: https://anonymous.4open.science/r/ReviewGNN-2E1E.

翻译：现代推荐系统通过融合用户行为图（经由图神经网络）与评论文本（经由大语言模型）可实现高性能。然而，这种融合面临三个显著问题：（1）对比学习中的假阴性样本会因惩罚相似物品而削弱训练信号；（2）常被编码为嵌入向量模长的流行度偏差会扭曲相似度评分；（3）源于评论中客观事实与主观情感混淆的信号模糊性。这些相互关联的问题会阻碍模型学习用户的真实偏好。本文提出SymCERE（对称SINCERE），一种通过其结构设计同时解决这三个问题的对比学习方法。首先，我们引入SINCERE损失在跨模态对齐中的对称应用，该设计旨在消除推荐中的假阴性样本。其次，通过在“模长即噪声”假设下将其与L2归一化相结合，我们迫使模型主要将偏好信息编码于向量方向，从而缓解流行度偏差。在来自三个不同平台（电子商务、本地点评和旅游）的15个数据集上的实验表明，SymCERE在NDCG@10指标上最高实现43.6%的相对提升，优于多个强基线模型。此外，详细的LIME分析显示，模型学会将对齐锚定于客观且信息丰富的词汇（如“OEM”、“兼容”、“垫圈”），同时减少对通用情感词（如“好”、“很棒”）的依赖。这表明有效的语义对齐源于对事实性产品属性的理解，为构建更精准的推荐系统提供了路径。代码公开于：https://anonymous.4open.science/r/ReviewGNN-2E1E。