# Balancing Efficiency and Effectiveness: An LLM-Infused Approach for Optimized CTR Prediction

arXiv: https://arxiv.org/abs/2412.06860 · HTML: https://mlsanity.com/paper/2412.06860/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 19 references that are in the map.
- First posted on arXiv 9 Dec 2024.

## Builds on (most cited first, 10 of 10)

- [LoRA: Low-Rank Adaptation of Large Language Models](https://mlsanity.com/paper/2106.09685/) (2021)
- [Deep Interest Network for Click-Through Rate Prediction](https://mlsanity.com/paper/1706.06978/) (2017)
- [Search-based User Interest Modeling with Lifelong Sequential Behavior Data for Click-Through Rate Prediction](https://mlsanity.com/paper/2006.05639/) (2020)
- [Towards Open-World Recommendation with Knowledge Augmentation from Large Language Models](https://mlsanity.com/paper/2306.10933/) (2023)
- [ClickPrompt: CTR Models are Strong Prompt Generators for Adapting Language Models to CTR Prediction](https://mlsanity.com/paper/2310.09234/) (2023)
- [Can Small Language Models be Good Reasoners for Sequential Recommendation?](https://mlsanity.com/paper/2403.04260/) (2024)
- [Breaking the Length Barrier: LLM-Enhanced CTR Prediction in Long Textual User Behaviors](https://mlsanity.com/paper/2403.19347/) (2024)
- [BERT4CTR: An Efficient Framework to Combine Pre-trained Language Model with Non-textual Features for CTR Prediction](https://mlsanity.com/paper/2308.11527/) (2023)
- [CAN: Feature Co-Action for Click-Through Rate Prediction](https://mlsanity.com/paper/2011.05625/) (2020)
- [Dynamic Few-Shot Learning for Knowledge Graph Question Answering](https://mlsanity.com/paper/2407.01409/) (2024)

## Built on by (most cited first, 3 of 3)

- [Hierarchical LoRA MoE for Efficient CTR Model Scaling](https://mlsanity.com/paper/2510.10432/) (2025)
- [CTR-Sink: Attention Sink for Language Models in Click-Through Rate Prediction](https://mlsanity.com/paper/2508.03668/) (2025)
- [RIA: A Ranking-Infused Approach for Optimized listwise CTR Prediction](https://mlsanity.com/paper/2511.21394/) (2025)

## Abstract (arXiv)

Click-Through Rate (CTR) prediction is essential in online advertising, where semantic information plays a pivotal role in shaping user decisions and enhancing CTR effectiveness. Capturing and modeling deep semantic information, such as a user's preference for "H"aagen-Dazs' HEAVEN strawberry light ice cream" due to its health-conscious and premium attributes, is challenging. Traditional semantic modeling often overlooks these intricate details at the user and item levels. To bridge this gap, we introduce a novel approach that models deep semantic information end-to-end, leveraging the comprehensive world knowledge capabilities of Large Language Models (LLMs). Our proposed LLM-infused CTR prediction framework(Multi-level Deep Semantic Information Infused CTR model via Distillation, MSD) is designed to uncover deep semantic insights by utilizing LLMs to extract and distill critical information into a smaller, more efficient model, enabling seamless end-to-end training and inference. Importantly, our framework is carefully designed to balance efficiency and effectiveness, ensuring that the model not only achieves high performance but also operates with optimal resource utilization. Online A/B tests conducted on the Meituan sponsored-search system demonstrate that our method significantly outperforms baseline models in terms of Cost Per Mile (CPM) and CTR, validating its effectiveness, scalability, and balanced approach in real-world applications.