Click-through rate (CTR) prediction is a fundamental task in modern recommender systems. In recent years, the integration of large language models (LLMs) has been shown to effectively enhance the performance of traditional CTR methods. However, existing LLM-enhanced methods often require extensive processing of detailed textual descriptions for large-scale instances or user/item entities, leading to substantial computational overhead. To address this challenge, this work introduces LLaCTR, a novel and lightweight LLM-enhanced CTR method that employs a field-level enhancement paradigm. Specifically, LLaCTR first utilizes LLMs to distill crucial and lightweight semantic knowledge from small-scale feature fields through self-supervised field-feature fine-tuning. Subsequently, it leverages this field-level semantic knowledge to enhance both feature representation and feature interactions. In our experiments, we integrate LLaCTR with six representative CTR models across four datasets, demonstrating its superior performance in terms of both effectiveness and efficiency compared to existing LLM-enhanced methods. Our code is available at https://github.com/istarryn/LLaCTR.

翻译：点击率（CTR）预测是现代推荐系统中的一项基础任务。近年来，大型语言模型（LLM）的集成已被证明能有效提升传统CTR方法的性能。然而，现有的LLM增强方法通常需要对大规模实例或用户/物品实体的详细文本描述进行大量处理，导致显著的计算开销。为应对这一挑战，本研究提出了LLaCTR，一种新颖且轻量级的LLM增强CTR方法，采用字段级增强范式。具体而言，LLaCTR首先利用LLM通过自监督的字段-特征微调，从小规模特征字段中提炼关键且轻量级的语义知识。随后，它利用这种字段级语义知识来增强特征表示和特征交互。在我们的实验中，我们在四个数据集上将LLaCTR与六种代表性CTR模型集成，证明了其在效果和效率方面均优于现有的LLM增强方法。我们的代码发布于https://github.com/istarryn/LLaCTR。