Baidu runs the largest commercial web search engine in China, serving hundreds of millions of online users every day in response to a great variety of queries. In order to build a high-efficiency sponsored search engine, we used to adopt a three-layer funnel-shaped structure to screen and sort hundreds of ads from billions of ad candidates subject to the requirement of low response latency and the restraints of computing resources. Given a user query, the top matching layer is responsible for providing semantically relevant ad candidates to the next layer, while the ranking layer at the bottom concerns more about business indicators (e.g., CPM, ROI, etc.) of those ads. The clear separation between the matching and ranking objectives results in a lower commercial return. The Mobius project has been established to address this serious issue. It is our first attempt to train the matching layer to consider CPM as an additional optimization objective besides the query-ad relevance, via directly predicting CTR (click-through rate) from billions of query-ad pairs. Specifically, this paper will elaborate on how we adopt active learning to overcome the insufficiency of click history at the matching layer when training our neural click networks offline, and how we use the SOTA ANN search technique for retrieving ads more efficiently (Here ``ANN'' stands for approximate nearest neighbor search). We contribute the solutions to Mobius-V1 as the first version of our next generation query-ad matching system.

翻译：百度运营着中国最大的商业网络搜索引擎，每日为数亿在线用户响应海量查询请求。为构建高效的搜索推广引擎，我们曾采用三层漏斗形结构，在低响应延迟要求和计算资源限制下，从数十亿广告候选中筛选并排序数百条广告。针对用户查询，顶层匹配层负责为下一层提供语义相关的广告候选，而底层的排序层则更关注广告的商业指标（如CPM、ROI等）。匹配目标与排序目标的明确分离导致了商业回报率的降低。MOBIUS项目正是为应对这一严峻问题而设立。这是我们首次尝试在匹配层训练中，除查询-广告相关性外，将CPM作为额外优化目标，通过直接从数十亿查询-广告对中预测CTR（点击率）来实现。具体而言，本文将详细阐述我们如何采用主动学习解决离线训练神经点击网络时匹配层点击历史数据不足的问题，以及如何利用SOTA ANN搜索技术实现更高效的广告检索（此处“ANN”指近似最近邻搜索）。我们将MOBIUS-V1的解决方案作为新一代查询-广告匹配系统的首个版本予以贡献。