TaoSR-AGRL: Adaptive Guided Reinforcement Learning Framework for E-commerce Search Relevance
arXiv:2510.08048v4 Announce Type: replace-cross Abstract: Query-product relevance prediction is fundamental to e-commerce search and has become even more critical in the era of AI-powered shopping, where semantic understanding and complex reasoning directly shape the user experience and business conversion. Large Language Models (LLMs) enable generative, reasoning-based approaches, typically aligned via supervised fine-tuning (SFT) or preference optimization methods like Direct Preference Optimization (DPO). However, the increasing complexity of business rules and user queries exposes the inab
The increasing complexity of e-commerce business rules and user queries, coupled with the limitations of current LLM alignment methods, drives the need for more adaptive relevance frameworks.
Improving e-commerce search relevance directly impacts user experience, conversion rates, and the effectiveness of AI in a critical commercial application.
This research introduces a new reinforcement learning framework for e-commerce search, potentially leading to more dynamic and context-aware product recommendations.
- · E-commerce platforms
- · AI/ML research labs
- · Consumers (improved search experience)
- · Online retailers
- · Platforms with static search algorithms
- · Businesses unable to implement advanced AI
E-commerce search results become more pertinent to user intent, increasing sales and customer satisfaction.
Broader adoption of reinforcement learning for dynamic content delivery beyond just search, leveraging user interaction data more effectively.
Increased competitive pressure on e-commerce platforms to continuously evolve their AI-driven customer interfaces, leading to an AI arms race in user experience optimization.
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Read at arXiv cs.AI