Deep neural networks have become a powerful technique for learning representations from user-item interaction data in collaborative filtering (CF) for recommender systems. However, many existing methods heavily rely on unique user and item IDs, which limits their ability to perform well in practical zero-shot learning scenarios where sufficient training data may be unavailable. Inspired by the success of language models (LMs) and their strong generalization capabilities, a crucial question arises: How can we harness the potential of language models to empower recommender systems and elevate its generalization capabilities to new heights? In this study, we propose EasyRec - an effective and easy-to-use approach that seamlessly integrates text-based semantic understanding with collaborative signals. EasyRec employs a text-behavior alignment framework, which combines contrastive learning with collaborative language model tuning, to ensure a strong alignment between the text-enhanced semantic space and the collaborative behavior information. Extensive empirical evaluations across diverse real-world datasets demonstrate the superior performance of EasyRec compared to state-of-the-art alternative models, particularly in the challenging text-based zero-shot recommendation scenarios. Furthermore, the study highlights the potential of seamlessly integrating EasyRec as a plug-and-play component into text-enhanced collaborative filtering frameworks, thereby empowering existing recommender systems to elevate their recommendation performance and adapt to the evolving user preferences in dynamic environments. For better result reproducibility of our EasyRec framework, the model implementation details, source code, and datasets are available at the link: https://github.com/HKUDS/EasyRec.

翻译：深度神经网络已成为从协同过滤（CF）中的用户-物品交互数据学习表示的有力技术。然而，许多现有方法严重依赖独特的用户和物品ID，这限制了它们在训练数据可能不足的实际零样本学习场景中的表现。受语言模型（LMs）的成功及其强大泛化能力的启发，一个关键问题随之产生：我们如何利用语言模型的潜力来赋能推荐系统，并将其泛化能力提升到新的高度？在本研究中，我们提出了EasyRec——一种有效且易于使用的方法，它将基于文本的语义理解与协同信号无缝集成。EasyRec采用文本-行为对齐框架，结合对比学习与协同语言模型微调，以确保文本增强的语义空间与协同行为信息之间的强对齐。在多个真实世界数据集上的广泛实证评估表明，EasyRec相较于最先进的替代模型具有优越性能，尤其是在具有挑战性的基于文本的零样本推荐场景中。此外，本研究强调了将EasyRec作为即插即用组件无缝集成到文本增强协同过滤框架中的潜力，从而使现有推荐系统能够提升其推荐性能，并适应动态环境中不断变化的用户偏好。为更好地复现EasyRec框架的结果，模型实现细节、源代码和数据集可在以下链接获取：https://github.com/HKUDS/EasyRec。