GACE: Learning Graph-Based Cross-Page Ads Embedding For Click-Through Rate Prediction
This work addresses cold-start and multi-page data integration for ads recommendation systems, showing incremental improvements in CTR prediction.
The paper tackled the problems of using multi-page historical advertising data and cold-start for new ads in click-through rate prediction, proposing GACE, a graph-based cross-page ads embedding method that increased click-through rates by 3.6%, 2.13%, and 3.02% on real-world pages and by 9.96%, 7.51%, and 8.97% in cold-start tasks.
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.