Problem
Research questions and friction points this paper is trying to address.
Auto-bidding
Advertiser Profiles
Personalization
Cold-start Adaptation
Profile Updating
Auto-Bidding with Disentangled Advertiser Profiles and Train-Free Adaptation
🤖 AI Summary
本文提出ADAPT框架，通过解耦广告主档案和无训练适应解决自动出价中档案构建与更新难题。
📝 Abstract
Auto-bidding is a key component of modern advertising systems that provides a personalized bidding strategy for each advertiser. By characterizing each individual, profile-based methods achieve personalization and have proven effective in domains such as recommendation. However, despite the diverse bidding behavior of advertisers, their application to auto-bidding remains limited. A primary reason is that constructing and leveraging advertiser profiles face several challenges: extracting pure profiles is non-trivial, modeling common and private information simultaneously is difficult, and profile updating and cold-start adaptation remain challenging. To tackle these issues, we propose \textbf{ADAPT}, an \underline{\textbf{A}}uto-bidding framework with \underline{\textbf{D}}isentangled \underline{\textbf{A}}dvertiser \underline{\textbf{P}}rofiles and \underline{\textbf{T}}raining-free adaptation. ADAPT introduces a two-stage training paradigm and supports training-free adaptation. Specifically, (i) the stage 1 extracts pure static and dynamic profiles via contrastive learning over the advertiser memory bank; (ii) the stage 2 disentangles the dynamic profile into a common profile and a private profile, and combines them with the static profile to jointly condition the bidding strategy; (iii) once trained, ADAPT constructs profiles for new advertisers and updates profiles of existing advertisers without retraining. Our experiments on a large-scale auto-bidding benchmark demonstrate that ADAPT consistently achieves superior performance, and ablation studies further validate the effectiveness of each module. The source code will be released at https://github.com/YuzunoKawori/ADAPT.
Innovation
Methods, ideas, or system contributions that make the work stand out.
disentangled advertiser profiles
training-free adaptation
contrastive learning
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