ADORE: Autonomous Domain-Oriented Relevance Engine for E-commerce

• URL: http://arxiv.org/abs/2512.02555v1
• Date: Tue, 02 Dec 2025 09:25:13 GMT
• Title: ADORE: Autonomous Domain-Oriented Relevance Engine for E-commerce
• Authors: Zheng Fang, Donghao Xie, Ming Pang, Chunyuan Yuan, Xue Jiang, Changping Peng, Zhangang Lin, Zheng Luo,
• Abstract summary: Relevance modeling in e-commerce search remains challenged by semantic gaps.<n>We propose ADORE, a self-sustaining framework that synergizes three innovations.<n>The framework establishes a new paradigm for resource-efficient, cognitively aligned relevance modeling in industrial applications.
• Score: 15.317195529037319
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Relevance modeling in e-commerce search remains challenged by semantic gaps in term-matching methods (e.g., BM25) and neural models' reliance on the scarcity of domain-specific hard samples. We propose ADORE, a self-sustaining framework that synergizes three innovations: (1) A Rule-aware Relevance Discrimination module, where a Chain-of-Thought LLM generates intent-aligned training data, refined via Kahneman-Tversky Optimization (KTO) to align with user behavior; (2) An Error-type-aware Data Synthesis module that auto-generates adversarial examples to harden robustness; and (3) A Key-attribute-enhanced Knowledge Distillation module that injects domain-specific attribute hierarchies into a deployable student model. ADORE automates annotation, adversarial generation, and distillation, overcoming data scarcity while enhancing reasoning. Large-scale experiments and online A/B testing verify the effectiveness of ADORE. The framework establishes a new paradigm for resource-efficient, cognitively aligned relevance modeling in industrial applications.

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