Information Systems
PRECTR-V2: Unified Relevance-CTR Framework with Cross-User Preference Mining, Exposure Bias Correction, and LLM-Distilled Encoder Optimization
This paper introduces PRECTR-V2, an advanced framework designed to enhance search relevance matching and click-through rate (CTR) prediction by addressing key limitations of prior systems. It unifies relevance and CTR objectives, tackles sparse user data with cross-user preference mining, corrects exposure bias through synthetic negative sampling, and optimizes encoder performance via LLM-distilled knowledge and fine-tuning. Extensive experiments demonstrate significant improvements in online and offline metrics.
Executive Impact: Transforming Enterprise Efficiency
Our analysis reveals significant opportunities for operational improvement and strategic advantage:
1.39%
Projected Annual Impact
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Lift in Per Capita Orders
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Increase in GMV
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AUC RelaImpr
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GAUC RelaImpr
Deep Analysis & Enterprise Applications
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Information Systems
Addressing Core Challenges in Search Systems
Modern search systems face the dual challenge of ensuring search relevance and maximizing conversion efficiency. Traditionally, these objectives were handled separately, leading to inconsistencies and suboptimal performance. PRECTR-V2 builds upon prior work to address three fundamental shortcomings:
1. Sparse User Data: Low-activity and new users have limited search behavioral data, making personalized relevance modeling difficult.
2. Exposure Bias: Training data primarily consists of high-relevance exposures, creating a distribution mismatch with broader candidate spaces.
3. Architectural Misalignment: The original Emb+MLP architecture with a frozen BERT encoder prevented joint optimization and led to misalignment between semantic representations and CTR fine-tuning.
PRECTR-V2 Unified Framework Overview
Cross-User Preference Mining
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Exposure Bias Correction
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LLM-Distilled Encoder Optimization
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Unified Relevance-CTR Prediction
Key Finding
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Achieved AUC Score
Offline Performance Comparison (AUC)
| Method | AUC | RelaImpr (AUC) |
|---|---|---|
| LR [13] |
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| DNN |
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| Wide&Deep [5] |
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| DeepFM [10] |
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| XDeepFM [18] |
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| DIN [35] |
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| SuKD [25] |
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| PRECTR [4] |
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| PRECTR-V2 |
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Online A/B Testing Success on Xianyu
PRECTR-V2 was successfully deployed online in Alibaba's second-hand online trading platform Xianyu. Through A/B testing, the experimental group (over 20% of total traffic) consistently outperformed the baseline. This led to a 1.39% lift in per capita orders and a 3.18% increase in GMV, demonstrating clear business impact and validating the method's effectiveness in a real-world setting. PRECTR-V2 is now fully deployed across Xianyu's search system.
Ablation Study Results
| Method | AUC | RI (AUC) | GAUC | RI (GAUC) |
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| PRECTR |
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| w/o Cold-Start Relevance Preference Mining |
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| w/o Relevance Exposure Debias |
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| w/o LLM-Distilled and CTR-Aligned Encoder |
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| PRECTR-V2 |
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Advanced ROI Calculator
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Projected Annual Savings
$0
Annual Hours Reclaimed
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Implementation Roadmap
Our structured approach ensures a seamless integration and optimal adoption of advanced AI capabilities within your enterprise.
Phase 1: Foundation & Data Integration
Establish core data pipelines for user behavior, query, and item features. Implement initial PRECTR-V2 framework with base encoder and relevance modeling components. Baseline performance evaluation.
Phase 2: Cross-User Preference Mining Deployment
Develop and integrate the cross-user relevance preference mining mechanism. Focus on cold-start user scenarios and evaluate improvements in personalized relevance modeling.
Phase 3: Exposure Bias Correction Rollout
Implement synthetic hard negative sampling, relevance label reconstruction, and pairwise ranking loss. Fine-tune debiasing parameters and monitor impact on generalization and CTR calibration.
Phase 4: LLM-Distilled Encoder Integration
Pretrain lightweight transformer-based encoder via LLM knowledge distillation and SFT. Replace frozen BERT and enable end-to-end joint optimization with CTR prediction.
Phase 5: A/B Testing & Full System Deployment
Conduct extensive online A/B tests to validate real-world effectiveness and business impact. Iterate on feedback and achieve full deployment across production search systems.
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