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Douyin Ads deploys GEAR for generative end-to-end ad retrieval

By PulseAugur Editorial · [1 sources] ·

Researchers have developed GEAR, a novel end-to-end framework designed to overcome critical bottlenecks in generative ad retrieval systems. The framework addresses representation collapse and item collisions, which hinder stable adaptation and precision in large-scale recommender systems. GEAR achieves this by jointly optimizing the tokenizer, generator, and reranker, incorporating innovations like BasisVQ for stable codebook parameterization and a context-conditioned reranking head to disambiguate items. This system is currently deployed on Douyin Ads, serving hundreds of millions of daily active users and demonstrating significant improvements in online A/B tests. AI

IMPACT Enhances scalability and precision in generative ad retrieval systems, impacting large-scale platforms like Douyin.

RANK_REASON Research paper detailing a new framework for ad retrieval. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.IR (Information Retrieval) →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

Douyin Ads deploys GEAR for generative end-to-end ad retrieval

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Research paper detailing a new framework for ad retrieval. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.IR (Information Retrieval) TIER_1 English(EN) · Xiang Sun ·

    Generative End-to-end Ad Retrieval at Douyin

    Generative retrieval reformulates recommendation as the generation of discrete item tokens. However, scaling this paradigm to real-world recommender systems reveals two critical bottlenecks: 1) Representation collapse, where the item tokenizer converges to degenerate results unde…

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