Click-through rate (CTR) prediction is one of the most central tasks in online advertising systems. Recent deep learning-based models that exploit feature embedding and high-order data nonlinearity have shown dramatic successes in CTR prediction. However, these models work poorly on cold-start ads with new IDs, whose embeddings are not well learned yet. In this paper, we propose Graph Meta Embedding (GME) models that can rapidly learn how to generate desirable initial embeddings for new ad IDs based on graph neural networks and meta learning. Previous works address this problem from the new ad itself, but ignore possibly useful information contained in existing old ads. In contrast, GMEs simultaneously consider two information sources: the new ad and existing old ads. For the new ad, GMEs exploit its associated attributes. For existing old ads, GMEs first build a graph to connect them with new ads, and then adaptively distill useful information. We propose three specific GMEs from different perspectives to explore what kind of information to use and how to distill information. In particular, GME-P uses Pre-trained neighbor ID embeddings, GME-G uses Generated neighbor ID embeddings and GME-A uses neighbor Attributes. Experimental results on three real-world datasets show that GMEs can significantly improve the prediction performance in both cold-start (i.e., no training data is available) and warm-up (i.e., a small number of training samples are collected) scenarios over five major deep learning-based CTR prediction models. GMEs can be applied to conversion rate (CVR) prediction as well.
翻译:点击率( CTR) 预测是在线广告系统中最核心的任务之一。 最近利用特征嵌入和高端数据无线性的深层次基于学习的模型在 CTR 预测中显示了巨大的成功。 然而,这些模型在使用新ID 的冷启动广告上效果不佳,而其嵌入还没有很好地了解。 在本文中,我们提议了图形Meta 嵌入模型(GME) 模型,这些模型可以快速学习如何根据图形神经网络和元学习为新的广告身份标识生成理想的初步嵌入。过去的工作从新广告本身解决了这一问题,但忽略了现有旧广告中可能包含的有用信息。相比之下,GME同时考虑两个信息来源:新的广告和现有的旧广告。对于现有的旧广告,GME首先建立一个图表,将它们与新广告连接起来,然后适应性地淡化有用的信息。我们从不同的角度提出了三个具体的GME 来探索如何使用GME 和如何提取信息。 特别是, GE- RE- Pread- traininging a reduvelop- disal- disal- disal- disporting disal- dislational- disleval disal sal lieval sals.