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Arxiv
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Enhanced Whole Page Optimization via Mixed-Grained Reward Mechanism-Adapted Language Models
- Whole Page Optimization (WPO) is crucial for improving user experience by optimizing search and recommendation results.
- Pre-trained Large Language Models (LLMs) are effective in generating relevant content, but fine-tuning them for complex tasks like WPO is challenging.
- This study introduces PageLLM, a reward-based fine-tuning approach for LLMs using user feedback as supervision.
- PageLLM utilizes a mixed-grained reward mechanism integrating page-level and item-level rewards to optimize presentation.
- Page-level reward assesses quality and coherence, while item-level reward focuses on accuracy and relevance of recommendations.
- User feedback is noisy and less precise compared to manually labeled datasets, posing a challenge that PageLLM addresses.
- PageLLM was tested on public and industrial datasets, surpassing baselines and showing a 0.44% GMV increase in an online A/B test.
- The dual-reward structure of PageLLM enhances both the overall quality and the individual components of WPO.
- Fine-tuning LLMs for WPO using user feedback reduces reliance on costly human-annotated data.
- PageLLM's success in real-world applications highlights its effectiveness in improving user engagement and system performance.
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