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.

本文提出了一种基于图的跨页面广告嵌入生成方法（GACE），通过多页历史广告数据的联合使用和新广告的冷启动来改善在线广告推荐系统，采用变分自编码器预训练模块生成新旧广告的嵌入表示，在公共数据集 AliEC 和阿里巴巴支付宝的真实行业数据集上进行评估，结果显示 GACE 方法明显优于现有方法，在线 A/B 测试中，支付宝三个真实页面的点击率分别增加了 3.6%、2.13%、3.02%，尤其在冷启动任务中，点击率分别增加了 9.96%、7.51%、8.97%。

GACE：学习基于图的跨页面广告嵌入用于点击率预测