Recommender Systems
Not All Candidates are Created Equal: A Heterogeneity-Aware Approach to Pre-ranking in Recommender Systems
This paper presents Heterogeneity-Aware Adaptive Pre-ranking (HAP), a novel framework for recommender systems. HAP addresses the challenge of heterogeneous candidates in pre-ranking by integrating Gradient-Harmonized Contrastive Learning (GHCL) for stable optimization and Difficulty-Aware Model Routing (DAMR) for adaptive resource allocation. Experiments show HAP significantly improves recommendation performance and reduces serving cost, making it a practical and scalable solution for industrial systems.
Executive Impact
HAP's approach directly translates to tangible business benefits by optimizing resource allocation and improving recommendation quality.
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Increase in User App Usage Duration
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Increase in User Active Days
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Reduction in CPU Usage
Deep Analysis & Enterprise Applications
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Gradient Conflicts & Heterogeneity
HAP Framework Overview
GHCL Effectiveness
DAMR Efficiency
Prevailing pre-ranking methods indiscriminately mix heterogeneous samples, leading to gradient conflicts where hard samples dominate training while easy ones are underutilized. This causes suboptimal performance and inefficient resource use.
HAP unifies Gradient-Harmonized Contrastive Learning (GHCL) and Difficulty-Aware Model Routing (DAMR) to address heterogeneity. GHCL mitigates gradient conflicts, while DAMR adaptively allocates computational budgets based on sample difficulty.
GHCL disentangles easy and hard samples, directing each subset along dedicated optimization paths and harmonizing gradient contributions. This balances easy and hard samples, ensuring comprehensive distributional coverage and preventing dominance by hard negatives.
DAMR employs a progressive routing strategy: a lightweight model processes all candidates for broad coverage, and difficult samples are forwarded to a more expressive model. This allocates computation efficiently within a fixed budget.
Suboptimal Performance
from Gradient Conflicts
Enterprise Process Flow
Heterogeneous Candidates
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GHCL (Gradient Harmonization)
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DAMR (Adaptive Routing)
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Lightweight Model (Easy Samples)
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Expressive Model (Hard Samples)
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Improved Pre-ranking
| Aspect | Traditional Methods | GHCL |
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| Sample Utilization |
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| Training Stability |
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Toutiao Production Deployment
HAP was deployed in Toutiao for 9 months, resulting in a 0.4% improvement in user app usage duration and 0.05% in active days without additional computational cost. This demonstrates HAP's practical benefits and scalability.
- +0.4% User App Usage Duration Increase
- +0.05% User Active Days Increase
- No Additional Cost Computational Cost
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Estimated Annual Savings
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Total Annual Hours Reclaimed
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