Deep neural networks have emerged as 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 restricts their performance in zero-shot learning scenarios. Inspired by the success of language models (LMs) and their robust generalization capabilities, we pose the question: How can we leverage language models to enhance recommender systems? We propose EasyRec, an effective approach that integrates text-based semantic understanding with collaborative signals. EasyRec employs a text-behavior alignment framework that combines contrastive learning with collaborative language model tuning. This ensures strong alignment between text-enhanced semantic representations and collaborative behavior information. Extensive evaluations across diverse datasets show EasyRec significantly outperforms state-of-the-art models, particularly in text-based zero-shot recommendation. EasyRec functions as a plug-and-play component that integrates seamlessly into collaborative filtering frameworks. This empowers existing systems with improved performance and adaptability to user preferences. Implementation codes are publicly available at: 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强大技术。然而，许多现有方法严重依赖唯一的用户和物品ID，这限制了它们在零样本学习场景中的性能。受语言模型（LMs）的成功及其强大泛化能力的启发，我们提出以下问题：如何利用语言模型来增强推荐系统？我们提出了EasyRec，一种将基于文本的语义理解与协同信号相结合的有效方法。EasyRec采用文本-行为对齐框架，将对比学习与协同语言模型调优相结合。这确保了文本增强的语义表示与协同行为信息之间的强对齐。跨多个数据集的广泛评估表明，EasyRec显著优于最先进的模型，特别是在基于文本的零样本推荐中。EasyRec作为一个即插即用组件，可无缝集成到协同过滤框架中。这使现有系统能够获得改进的性能和对用户偏好的适应能力。实现代码公开于：https://github.com/HKUDS/EasyRec。