GACE: Learning Graph-Based Cross-Page Ads Embedding For Click-Through
  Rate Prediction
Haowen Wang Yuliang Du Congyun Jin Yujiao Li Yingbo Wang Tao Sun Piqi Qin Cong Fan
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.