Field Matters: A lightweight LLM-enhanced Method for CTR Prediction
This work addresses efficiency challenges in LLM-enhanced CTR prediction for recommender systems, representing an incremental improvement over prior methods.
The paper tackles the computational overhead of integrating large language models (LLMs) into click-through rate (CTR) prediction by proposing LLaCTR, a lightweight method that uses field-level semantic knowledge, achieving superior performance and efficiency compared to existing LLM-enhanced methods across six models and four datasets.
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://anonymous.4open.science/r/LLaCTR-EC46.