Summary of Balancing Efficiency and Effectiveness: An Llm-infused Approach For Optimized Ctr Prediction, by Guoxiao Zhang et al.
Balancing Efficiency and Effectiveness: An LLM-Infused Approach for Optimized CTR Prediction
by Guoxiao Zhang, Yi Wei, Yadong Zhang, Huajian Feng, Qiang Liu
First submitted to arxiv on: 9 Dec 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed framework, Multi-level Deep Semantic Information Infused CTR model via Distillation (MSD), leverages Large Language Models (LLMs) to predict Click-Through Rate (CTR) in online advertising. By capturing deep semantic information at both the user and item levels, MSD models intricate details such as a user’s preference for Häagen-Dazs’ HEAVEN strawberry light ice cream due to its health-conscious and premium attributes. The framework is designed to balance efficiency and effectiveness, achieving high performance while operating with optimal resource utilization. Online A/B tests on Meituan’s sponsored-search system demonstrate that MSD outperforms baseline models in terms of Cost Per Mile (CPM) and CTR. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary In online advertising, predicting what users will click is crucial. This paper introduces a new approach to help make this prediction more accurate. It uses large language models to understand the meaning behind words like “Häagen-Dazs” and “strawberry light ice cream”. The model is designed to learn from these complex relationships and make predictions that are both effective and efficient. Tests on real-world data show that this approach outperforms other methods, making it a valuable tool for advertisers. |
Keywords
» Artificial intelligence » Distillation
