CELA：用于CTR预测的成本高效语言模型对齐
CELA: Cost-Efficient Language Model Alignment for CTR Prediction
AI总结：
针对CTR预测中冷启动和单模态知识利用不足的问题，提出CELA框架，整合PLMs与基于ID的模型，通过硬提示将特征转为文本，保留协同过滤效果并提升效率，离线和在线实验证明其优于现有方法。
摘要：
点击率（CTR）预测在推荐系统中占据至关重要的地位。由于特征频率分布不均衡，主流的基于ID的范式在冷启动场景下表现不佳。此外，单一模态的使用无法充分利用文本特征中包含的知识。近期研究试图通过整合预训练语言模型（PLMs）来缓解这些挑战，它们设计硬提示将每个交互的原始特征结构化为文本，然后应用PLMs进行文本处理。凭借外部知识和推理能力，PLMs即使在交互稀疏的情况下也能提取有价值的信息。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，同时影响训练和推理过程中的特征可扩展性与效率。为此，我们提出CELA（成本高效语言模型对齐）用于CTR预测。CELA整合文本特征和语言模型，同时保留基于ID的模型的协同过滤能力。这种模型无关的框架可配备即插即用的文本特征，通过项级对齐增强外部信息的利用，同时维持训练和推理效率。通过大量离线实验，CELA表现出优于现有最先进方法的性能。此外，在工业级App推荐系统上进行的在线A/B测试展示了其实用有效性，巩固了CELA在实际应用中的潜力。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
