PlatformBid: An Auto-Bidding Benchmark from a Unified Advertising Platform's Perspective

AI 中文解读

1. 核心亮点：首个从广告平台整体视角出发的自动竞价评测系统，还带来了一个叫BidFlow的新方法，在真实平台上让广告成本降低了0.68%。 2. 通俗解读：你在刷短视频或购物时看到的广告，背后其实有一套自动出价系统，广告主设定预算和目标，系统帮忙抢合适的广告位。以前的研究都只盯着“替广告主省钱”这一头，但现在像快手这类大平台自己既卖广告位又当中间商，还得考虑平台总收益。PlatformBid就是专门用来测试这种“既要也要”场景的考场，设计了三种真实情况：大家都用同样的策略、有人聪明有人笨、大促期间广告主疯狂加预算。BidFlow则是用了一种叫“流匹配”的新技术，能灵活应对竞争对手的变化，像老司机一样随机应变。 3. 实际影响：以后你在电商大促时看到的广告会更精准，广告主花更少的钱获得更多曝光，平台也能赚得更稳。对小商家来说，即使不懂复杂竞价策略，系统也能自动帮你找到性价比最高的投放方式，避免盲目烧钱。而对普通用户，广告体验可能更顺眼，因为系统整体优化了，不会老推些没用的东西。

arXiv:2607.27265v1 Announce Type: new Abstract: Real-time bidding is central to computational advertising, comprising three elements: Supply Side Platform (SSP) selling ad impressions, Demand Side Platform (DSP) bidding for advertisers, and Ad Exchange conducting auctions between them. Traditional auto-bidding algorithms focus solely on the DSP side, maximizing advertiser conversions by adjusting bids against competitors. However, current big ad platforms, such as social media and e-commerce companies, now integrate SSP, DSP, and Ad Exchange functions internally. From such ad platforms' perspective, the goal of the auto-bidding algorithms is not only to maximize the advertisers' conversions, but also the total revenue of the platform. Given the lack of platform-centric evaluation frameworks and the pressing need to advance auto-bidding research, we propose PlatformBid - the first comprehensive benchmark designed from a unified ad platform's perspective. To accurately reflect the real-world auto-bidding scenarios, we define three representative settings: (1) homogeneous competition with identical algorithms across advertisers, (2) heterogeneous competition with diverse algorithmic strategies, and (3) promotional competition where some advertisers surge budgets for boosting sales during promotional events like Black Friday. We systematically evaluate a broad spectrum of existing auto-bidding methods across these settings, encompassing classical control methods, RL-based methods, and recent generative methods. Besides these methods, we further propose a novel auto-bidding method based on flow-matching, termed BidFlow, which leverages the flow-matching method's expressive policy representation to effectively handle dynamic competitive environments. Online experiments on Kuaishou further show a +0.68\% improvement in target cost, providing deployment evidence for the offline-online consistency of PlatformBid.