Predicting click-through rate (CTR) is the core task of many ads online recommendation systems, which helps improve user experience and increase platform revenue. In this type of recommendation system, we often encounter two main problems: the joint usage of multi-page historical advertising data and the cold start of new ads. In this paper, we proposed GACE, a graph-based cross-page ads embedding generation method. It can warm up and generate the representation embedding of cold-start and existing ads across various pages. Specifically, we carefully build linkages and a weighted undirected graph model considering semantic and page-type attributes to guide the direction of feature fusion and generation. We designed a variational auto-encoding task as pre-training module and generated embedding representations for new and old ads based on this task. The results evaluated in the public dataset AliEC from RecBole and the real-world industry dataset from Alipay show that our GACE method is significantly superior to the SOTA method. In the online A/B test, the click-through rate on three real-world pages from Alipay has increased by 3.6%, 2.13%, and 3.02%, respectively. Especially in the cold-start task, the CTR increased by 9.96%, 7.51%, and 8.97%, respectively.

翻译：预测点击率（CTR）是许多在线广告推荐系统的核心任务，有助于提升用户体验和增加平台收益。在此类推荐系统中，我们常面临两个主要问题：多页面历史广告数据的联合使用与新广告的冷启动。本文提出了GACE，一种基于图的跨页面广告嵌入生成方法。该方法能够跨不同页面预热并生成冷启动广告及现有广告的表示嵌入。具体而言，我们精心构建了考虑语义和页面类型属性的关联及加权无向图模型，以指导特征融合与生成方向。我们设计了变分自编码任务作为预训练模块，并基于该任务为新旧广告生成嵌入表示。在RecBole提供的公共数据集AliEC及支付宝真实工业数据集上的评估结果表明，GACE方法显著优于当前最先进（SOTA）方法。在线A/B测试中，支付宝三个真实页面的点击率分别提升了3.6%、2.13%和3.02%。尤其在冷启动任务中，点击率分别提升了9.96%、7.51%和8.97%。