mlsanityData to 14 Oct 2026

Field Matters: A Lightweight LLM-enhanced Method for CTR Prediction

arXiv 2505.14057 ↗First posted 20 May 2025cs.IR cs.AIcited by 4

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Abstract (arXiv)

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.

Key facts. Cited by 4 papers in the map (data through 14 Oct 2026). Builds on 75 of its 102 references that are in the map. First posted on arXiv 20 May 2025.

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75 of 102 references.

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2026: 4.

4 papers.

  1. 2026Talos: Optimizing Top-K Accuracy in Recommender Systemscited by 1
  2. 2026BEAR: Towards Beam-Search-Aware Optimization for Recommendation with Large Language Modelscited by 1
  3. 2026The Pitfall of Scaling Up: Uncovering and Mitigating Popularity Bias Amplification in Scaling Transformer-based Recommenderscited by 0
  4. 2026SpecFormer: Mitigating Embedding and Attention Collapse via Spectral-Aware Transformer for Recommendationcited by 0