GACE：学习基于图的跨页面广告嵌入用于点击率预测
GACE: Learning Graph-Based Cross-Page Ads Embedding For Click-Through Rate Prediction
AI总结：
本文提出GACE方法，通过构建融合语义和页面属性的图模型并结合变分自编码预训练，为跨页面广告生成嵌入表示，有效解决多页面数据联合使用和新广告冷启动问题，在公开及真实数据集上显著提升CTR。
摘要：
预测点击率（CTR）是许多在线广告推荐系统的核心任务，有助于改善用户体验并提高平台收入。在这类推荐系统中，我们经常遇到两个主要问题：多页面历史广告数据的联合使用以及新广告的冷启动。在本文中，我们提出了GACE，一种基于图的跨页面广告嵌入生成方法。它能够为不同页面上的冷启动广告和已有广告进行预热并生成表示嵌入。具体而言，我们仔细构建了链接关系和一个考虑语义属性与页面类型属性的加权无向图模型，以引导特征融合和生成的方向。我们设计了一个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。在来自RecBole的公开数据集AliEC和来自支付宝的真实行业数据集上的评估结果表明，我们的GACE方法显著优于SOTA方法。在在线A/B测试中，支付宝三个真实页面的点击率分别提高了3.6%、2.13%和3.02%。特别是在冷启动任务中，CTR分别提高了9.96%、7.51%和8.97%。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
