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

TL;DR

This paper presents a novel LLM-infused CTR prediction framework that captures deep semantic information efficiently, improving performance while maintaining resource balance, validated through online A/B testing.

Contribution

The paper introduces MSD, a framework that leverages LLMs for deep semantic modeling in CTR prediction, balancing effectiveness and efficiency with end-to-end training.

Findings

Significantly outperforms baseline models in CPM and CTR.

Demonstrates scalability and real-world effectiveness.

Balances high performance with resource efficiency.

Abstract

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.