用轻量字段增强提升点击率预测，兼顾效果与效率
Field Matters: A Lightweight LLM-enhanced Method for CTR Prediction
- 在字段层面用LLM提取轻量语义知识，降低计算开销
- 在4个数据集上优于现有方法，且推理速度更快
- 适合需要高效部署的推荐系统场景
点击率（CTR）预测是现代推荐系统的核心任务。近年来，大型语言模型（LLMs）被证明能有效提升传统CTR方法的性能，但现有方法通常需对大规模实例或用户/物品实体的详细文本进行复杂处理，带来显著计算负担。为此，本文提出LLaCTR，一种新颖且轻量的LLM增强型CTR方法，采用字段级增强范式：首先通过自监督字段-特征微调，让LLM从小型特征字段中提炼出关键、轻量的语义知识；随后利用该知识增强特征表示与特征交互。我们在四个数据集上将LLaCTR集成至六种代表性CTR模型，结果表明其在有效性与效率方面均优于现有方法。代码已开源：https://github.com/istarryn/LLaCTR。
原文摘要 · Abstract (English)
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.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。