A SOFT-PARTITIONED SEMI-SUPERVISED COLLABORATIVE TRANSFER LEARNING APPROACH FOR MULTI-DOMAIN RECOMMENDATION
Revolutionizing Multi-Domain Recommendation with Adaptive Soft-Partitioning and Transfer Learning
This paper introduces Soft-partitioned Semi-supervised Collaborative Transfer Learning (SSCTL), a novel approach for multi-domain recommendation. SSCTL addresses data imbalance and overfitting challenges in shared-specific architectures by dynamically generating parameters and leveraging pseudo-labels. Experiments show significant online and offline performance improvements, including GMV increases from 0.54% to 2.90% and CTR enhancements from 0.22% to 1.69%.
Quantifiable Impact for Your Enterprise
SSCTL delivers tangible improvements in key e-commerce metrics, translating directly to enhanced revenue and user engagement across diverse domains.
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Max GMV Increase (Online)
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Max CTR Increase (Online)
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Max AUC RImp (Offline, Sparse Domain)
Deep Analysis & Enterprise Applications
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Methodology
Performance
Challenges & Solutions
Methodology Overview
SSCTL proposes a novel soft-partitioning method that autonomously extracts domain information, contrasting the traditional hard-partitioning approach. It introduces Instance Soft-partitioned Collaborative Training (ISCT) for overfitting in specific parameters and Soft-partitioned Domain Differentiation Network (SDDN) for shared parameters, leveraging dominant domain data as unlabeled, generating pseudo-labels, and dynamically generating parameters based on domain information. This innovative approach enhances multi-domain recommendation performance.
Enterprise Process Flow
Input Features
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Feature Embedding Layer
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Domain-Shared Experts
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Domain-Specific Experts
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Gating Network
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Soft-partitioned Domain Differentiation Network (SDDN)
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Instance Soft-partitioned Collaborative Training (ISCT)
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Tower for Prediction
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Predicted User Behavior Label
Understanding Soft-Partitioning
Concept: Traditional multi-domain recommendation relies on hard-partitioning by using domain indicators. However, SSCTL introduces 'soft-partitioning' where a domain probability distribution is generated for each sample, capturing nuanced domain-related information beyond simple indicators.
Explanation: This allows for more effective network differentiation and integrates knowledge from non-dominant domains even when the true label belongs to the dominant domain.
Key Takeaway: Soft-partitioning enables a more flexible and robust differentiation of network parameters, especially crucial in scenarios with complex and overlapping domain characteristics, moving beyond rigid, predefined domain boundaries.
Application Context: This is particularly beneficial in real-world e-commerce where user behavior is not strictly confined to pre-defined domain categories, and items or services often appear across multiple sub-domains.
Enterprise Impact: Enterprises can achieve more adaptive and accurate recommendation systems, leading to higher engagement and conversion rates across diverse product or service categories without relying on manual, rigid domain definitions.
Performance Highlights
SSCTL demonstrated superior performance across various metrics and datasets. Offline experiments on Ali-CCP and MT-takeaway datasets showed SSCTL consistently outperforming baselines in AUC, particularly in sparse non-dominant domains. Online A/B tests on the Meituan Takeaway platform confirmed these results, with GMV increases ranging from 0.54% to 2.90% and CTR enhancements from 0.22% to 1.69%.
2.90%
Max GMV increase in online A/B tests
Context: SSCTL achieved significant improvements in Gross Merchandise Volume across various domains during a 10-day online A/B test on the Meituan Takeaway platform. This indicates a direct positive impact on business revenue.
Enterprise Significance: A nearly 3% increase in GMV represents substantial additional revenue for e-commerce platforms, validating the practical effectiveness and ROI of SSCTL in real-world, high-stakes environments.
1.69%
Max CTR increase in online A/B tests
Context: Click-Through Rate also saw notable enhancements, indicating that recommendations powered by SSCTL are more relevant and engaging for users. This improvement reflects better user experience and content discoverability.
Enterprise Significance: Increased CTR translates to more user engagement, which can lead to higher conversion rates and overall platform activity. It suggests that SSCTL effectively personalizes recommendations, making them more appealing to the end-users.
| Domain | Baseline (Avg. AUC) | SSCTL (AUC) | SSCTL RImp (%) |
|---|---|---|---|
| D1 (Dominant) | 0.6938 | 0.6951 | +0.30% |
| D2 | 0.6758 | 0.6783 | +4.35% |
| D3 | 0.7225 | 0.7245 | +1.89% |
| D4 | 0.6858 | 0.6907 | +5.31% |
| D5 | 0.6541 | 0.6591 | +6.19% |
| D6 (Sparse) | 0.6722 | 0.6817 | +12.47% |
Note: SSCTL consistently outperforms baselines, especially in sparse non-dominant domains, demonstrating robustness. |
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Addressing Key Challenges
SSCTL effectively addresses two critical challenges in multi-domain recommendation: Overwhelming and Overfitting. Overwhelming, where dominant domain data skews model performance, is mitigated by dynamically generated parameters that shift focus to non-dominant samples. Overfitting, caused by sparse data in non-dominant domains, is combated by leveraging pseudo-labels with weights from dominant domain instances to enrich data.
Addressing Data Imbalance with SSCTL
Problem: In real-world e-commerce, dominant domains (e.g., homepage) often account for over 80% of traffic, leading to highly imbalanced data. This causes 'overwhelming' where shared parameters are skewed towards dominant domains, neglecting non-dominant ones, and 'overfitting' in specific parameters of sparse non-dominant domains.
Solution: SSCTL introduces two modules: Instance Soft-partitioned Collaborative Training (ISCT) and Soft-partitioned Domain Differentiation Network (SDDN). ISCT treats dominant domain data as unlabeled to generate weighted pseudo-labels, enriching non-dominant domain data and preventing overfitting. SDDN uses soft-partitioning to dynamically generate parameters, shifting focus to non-dominant domains and reducing the overwhelming effect.
Outcome: Offline experiments showed SSCTL significantly improved performance in sparse non-dominant domains (e.g., Domain D6 with +12.47% RImp in AUC). Online A/B tests confirmed this with consistent GMV and CTR uplifts across all domains, including the less dominant ones.
Enterprise Value: This allows enterprises to derive significant value from all their sub-domains, not just the largest ones. By addressing data imbalance and sparsity, SSCTL ensures that smaller, niche categories receive effective recommendations, unlocking new revenue streams and improving overall user satisfaction across the entire platform. It moves beyond a 'one-size-fits-all' approach to truly optimize diverse offerings.
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Estimated Annual Savings
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Annual Hours Reclaimed
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Your Implementation Roadmap
A clear path to integrate SSCTL and unlock its full potential within your enterprise, from discovery to full-scale deployment.
Discovery & Data Prep (2-4 Weeks)
Understand existing recommendation systems, identify data sources, and prepare multi-domain datasets, including handling data imbalance and sparsity.
Model Adaptation & Training (4-8 Weeks)
Adapt SSCTL architecture to enterprise specific features. Implement ISCT for pseudo-label generation and SDDN for dynamic parameter tuning. Train and validate models on historical data.
A/B Testing & Refinement (2-4 Weeks)
Deploy SSCTL in a controlled online A/B test environment. Monitor key metrics (GMV, CTR) and iteratively refine model parameters based on real-world user feedback and performance.
Full-Scale Deployment & Monitoring (Ongoing)
Roll out SSCTL across all relevant domains. Establish continuous monitoring for performance, data drift, and user satisfaction to ensure sustained improvements.
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