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

arXiv: https://arxiv.org/abs/2512.02555 · HTML: https://mlsanity.com/paper/2512.02555/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 15 of its 25 references that are in the map.
- First posted on arXiv 2 Dec 2025.

## Builds on (most cited first, 15 of 15)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Proximal Policy Optimization Algorithms](https://mlsanity.com/paper/1707.06347/) (2017)
- [The Llama 3 Herd of Models](https://mlsanity.com/paper/2407.21783/) (2024)
- [Direct Preference Optimization: Your Language Model is Secretly a Reward Model](https://mlsanity.com/paper/2305.18290/) (2023)
- [Large language models can accurately predict searcher preferences](https://mlsanity.com/paper/2309.10621/) (2023)
- [Text Matching as Image Recognition](https://mlsanity.com/paper/1602.06359/) (2016)
- [Match-SRNN: Modeling the Recursive Matching Structure with Spatial RNN](https://mlsanity.com/paper/1604.04378/) (2016)
- [Generative Retrieval and Alignment Model: A New Paradigm for E-commerce Retrieval](https://mlsanity.com/paper/2504.01403/) (2025)
- [Improving Pinterest Search Relevance Using Large Language Models](https://mlsanity.com/paper/2410.17152/) (2024)
- [A Semi-supervised Multi-channel Graph Convolutional Network for Query Classification in E-commerce](https://mlsanity.com/paper/2408.01928/) (2024)
- [Beyond Semantics: Learning a Behavior Augmented Relevance Model with Self-supervised Learning](https://mlsanity.com/paper/2308.05379/) (2023)
- [Deep Bag-of-Words Model: An Efficient and Interpretable Relevance Architecture for Chinese E-Commerce](https://mlsanity.com/paper/2407.09395/) (2024)
- [Robust Interaction-Based Relevance Modeling for Online e-Commerce Search](https://mlsanity.com/paper/2406.02135/) (2024)
- [Boosting LLM-based Relevance Modeling with Distribution-Aware Robust Learning](https://mlsanity.com/paper/2412.12504/) (2024)
- [CPRM: A LLM-based Continual Pre-training Framework for Relevance Modeling in Commercial Search](https://mlsanity.com/paper/2412.01269/) (2024)

## Built on by (most cited first, 2 of 2)

- [RAIR: A Rule-Aware Benchmark Uniting Challenging Long-Tail and Visual Salience Subset for E-commerce Relevance Assessment](https://mlsanity.com/paper/2512.24943/) (2025)
- [RAG-Match: Retrieval-Augmented Knowledge Injection and Hierarchical Reasoning for Calibrated Semantic Relevance](https://mlsanity.com/paper/2605.25486/) (2026)

## Abstract (arXiv)

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.