Auto-Bidding with Disentangled Advertiser Profiles and Train-Free Adaptation

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 shared and private information simultaneously is difficult, and profile updating and cold-start adaptation remain challenging. To tackle these issues, we propose **ADAPT**, an **A**uto-bidding framework with **D**isentangled **A**dvertiser **P**rofiles and **T**raining-free adaptation. ADAPT introduces a two-stage training paradigm and supports training-free adaptation. Specifically, (i) the first stage extracts pure stable and dynamic profiles via contrastive learning over the advertiser memory table; (ii) the second stage disentangles the dynamic profile into a shared strategy and a private strategy, 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.