Traditional recommender systems have heavily relied on identity representations (IDs) to model users and items, while the ascendancy of pre-trained language model (PLM) encoders has enriched the modeling of contextual item descriptions. However, PLMs, although effective in addressing few-shot, zero-shot, or unified modeling scenarios, often neglect the crucial collaborative filtering signal. This neglect gives rise to two pressing challenges: (1) Collaborative Contextualization, the seamless integration of collaborative signals with contextual representations. (2) the imperative to bridge the representation gap between ID-based representations and contextual representations while preserving their contextual semantics. In this paper, we propose CollabContext, a novel model that adeptly combines collaborative filtering signals with contextual representations and aligns these representations within the contextual space, preserving essential contextual semantics. Experimental results across three real-world datasets demonstrate substantial improvements. Leveraging collaborative contextualization, CollabContext can also be effectively applied to cold-start scenarios, achieving remarkable enhancements in recommendation performance. The code is available after the conference accepts the paper.

翻译：传统推荐系统严重依赖身份表示（ID）来建模用户与物品，而预训练语言模型（PLM）编码器的崛起则丰富了上下文物品描述的建模能力。然而，尽管PLM在解决少样本、零样本或统一建模场景中表现有效，却常常忽视关键的协同过滤信号。这一疏忽引发了两个紧迫的挑战：（1）协同上下文化——如何将协同信号与上下文表示无缝整合；（2）亟需弥合基于ID的表示与上下文表示之间的表征鸿沟，同时保留其上下文语义。本文提出CollabContext这一新颖模型，该模型巧妙地将协同过滤信号与上下文表示相结合，并在上下文空间中对齐这些表示，从而保留关键上下文语义。在三个真实世界数据集上的实验结果表明其取得了显著改进。通过利用协同上下文化，CollabContext还可在冷启动场景中有效应用，实现推荐性能的显著提升。代码将于论文被会议录用后公开。