Click-Through Rate (CTR) prediction holds a paramount position in recommender systems. The prevailing ID-based paradigm underperforms in cold-start scenarios due to the skewed distribution of feature frequency. Additionally, the utilization of a single modality fails to exploit the knowledge contained within textual features. Recent efforts have sought to mitigate these challenges by integrating Pre-trained Language Models (PLMs). They design hard prompts to structure raw features into text for each interaction and then apply PLMs for text processing. With external knowledge and reasoning capabilities, PLMs extract valuable information even in cases of sparse interactions. Nevertheless, compared to ID-based models, pure text modeling degrades the efficacy of collaborative filtering, as well as feature scalability and efficiency during both training and inference. To address these issues, we propose \textbf{C}ost-\textbf{E}fficient \textbf{L}anguage Model \textbf{A}lignment (\textbf{CELA}) for CTR prediction. CELA incorporates textual features and language models while preserving the collaborative filtering capabilities of ID-based models. This model-agnostic framework can be equipped with plug-and-play textual features, with item-level alignment enhancing the utilization of external information while maintaining training and inference efficiency. Through extensive offline experiments, CELA demonstrates superior performance compared to state-of-the-art methods. Furthermore, an online A/B test conducted on an industrial App recommender system showcases its practical effectiveness, solidifying the potential for real-world applications of CELA.

翻译：点击率（CTR）预测在推荐系统中占据核心地位。由于特征频次分布偏斜，主流的基于ID的范式在冷启动场景中表现不佳。此外，单一模态的利用未能充分挖掘文本特征中所蕴含的知识。近期研究尝试通过集成预训练语言模型（PLMs）来缓解这些挑战，通过设计硬提示（hard prompts）将原始特征结构化为每次交互的文本，并应用PLMs进行文本处理。借助外部知识与推理能力，PLMs即使在交互稀疏的情况下也能提取有价值信息。然而，与基于ID的模型相比，纯文本建模会降低协同过滤的效果，并在训练和推理阶段影响特征的可扩展性与效率。为解决这些问题，我们提出面向CTR预测的高效语言模型对齐方法（CELA）。CELA在保留基于ID模型协同过滤能力的同时，融合了文本特征与语言模型。这一模型无关框架可即插即用地引入文本特征，通过项目级对齐增强外部信息的利用，同时保持训练和推理效率。通过大量离线实验，CELA展现出优于现有最先进方法的性能。此外，在某工业级应用推荐系统上的在线A/B测试验证了其实际有效性，进一步夯实了CELA在真实场景中的潜力。