Traditional recommender systems primarily leverage identity-based (ID) representations for users and items, while the advent of pre-trained language models (PLMs) has introduced rich semantic modeling of item descriptions. However, PLMs often overlook the vital collaborative filtering signals, leading to challenges in merging collaborative and semantic representation spaces and fine-tuning semantic representations for better alignment with warm-start conditions. Our work introduces CARec, a cutting-edge model that integrates collaborative filtering with semantic representations, ensuring the alignment of these representations within the semantic space while retaining key semantics. Our experiments across four real-world datasets show significant performance improvements. CARec's collaborative alignment approach also extends its applicability to cold-start scenarios, where it demonstrates notable enhancements in recommendation accuracy. The code will be available upon paper acceptance.

翻译：传统推荐系统主要利用基于身份（ID）的用户和物品表征，而预训练语言模型（PLMs）的兴起引入了丰富的物品描述语义建模。然而，PLMs常常忽略关键的协同过滤信号，导致在融合协同表征空间与语义表征空间、以及微调语义表征以更好地适应热启动条件时面临挑战。本研究提出CARec，一种将协同过滤与语义表征相集成的尖端模型，确保在语义空间中对齐这些表征的同时保留核心语义信息。我们在四个真实世界数据集上的实验显示出显著的性能提升。CARec的协同对齐方法还拓展其在冷启动场景中的适用性，并在该场景下表现出推荐准确率的显著增强。论文被接收后将公开相关代码。