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References (27)
[3]
COPR: Consistency-Oriented Pre-Ranking for Online Advertising
Founder's Pitch
"Develop a heterogeneity-aware pre-ranking system for recommender systems to enhance efficiency and accuracy without additional computational cost."
Commercial Viability Breakdown
0-10 scaleHigh Potential
1/4 signals
Quick Build
4/4 signals
Series A Potential
4/4 signals
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Why It Matters
Recommender systems suffer from inefficiency and suboptimal performance due to the handling of heterogeneous samples. This research addresses these core issues by providing a tailored approach to distinguishing easy and hard samples, optimizing the system significantly without boosting costs, which is crucial for large-scale application.
Product Angle
Create a plugin or API for existing recommender systems that implement HAP, allowing large platforms like e-commerce sites or streaming services to integrate this efficient pre-ranking system into their workflows.
Disruption
HAP could replace less efficient pre-ranking strategies in current recommender systems, offering a more balanced and cost-efficient solution.
Product Opportunity
The global market for recommender systems is vast, spanning e-commerce, streaming, and social media, where each enhanced user engagement directly leads to revenue. Companies will pay for tools that improve recommendation precision without added computational costs.
Use Case Idea
Implement HAP in online streaming platforms to better filter content for user recommendations, enhancing user engagement without increasing backend processing costs.
Science
This paper introduces Heterogeneity-Aware Adaptive Pre-ranking (HAP), a framework that separates easy and hard samples for different optimization paths in a recommender system's pre-ranking stage. By using lightweight models for easy samples and more robust models for hard samples, the system efficiently manages computational resources while maintaining high accuracy.
Method & Eval
HAP was tested through deployment in Toutiao's recommendation system. Over nine months, it achieved increased user engagement metrics, specifically a 0.4% improvement in app usage and a 0.05% uptick in active user days, all without extra computational cost.
Caveats
The methodology might be heavily tailored to Toutiao's existing infrastructure; adapting it to other platforms may require significant customization. The improvements, while important at scale, may be perceived as marginal on smaller platforms.
Author Intelligence
Pengfei Tong
ByteDance
tongpengfei@bytedance.com
Siyuan Chen
ByteDance
Chenwei Zhang
ByteDance
Bo Wang
ByteDance
wangbo.9830@bytedance.com
Qi Pi
ByteDance
wk@bytedance.com
Pixun Li
ByteDance
lipixun@bytedance.com
Zuotao Liu
ByteDance
michael.liu@bytedance.com