论文标题
软重定网络以获取速率预测的单击
Soft Retargeting Network for Click Through Rate Prediction
论文作者
论文摘要
对用户兴趣模型的研究最近在点击率（CTR）预测中受到了很多关注。这些模型旨在从不同的角度捕获用户兴趣，包括用户兴趣发展，会话兴趣，多个兴趣等。在本文中，我们专注于一种新型的用户兴趣，即用户重新定位兴趣。用户重新定位兴趣定义为用户对目标项目的点击兴趣，与历史点击项目相同或相似。我们提出了一个新型的软重定网络（SRN），以模拟这一特定兴趣。具体而言，我们首先借助图嵌入来计算目标项目与每个历史项目之间的相似性。然后，我们学会汇总相似权重，以衡量用户对目标项目的点击兴趣的程度。此外，我们对用户重新定位兴趣的演变进行建模。公共数据集和工业数据集的实验结果表明，我们的模型比最先进的模型取得了重大改进。
The study of user interest models has received a great deal of attention in click through rate (CTR) prediction recently. These models aim at capturing user interest from different perspectives, including user interest evolution, session interest, multiple interests, etc. In this paper, we focus on a new type of user interest, i.e., user retargeting interest. User retargeting interest is defined as user's click interest on target items the same as or similar to historical click items. We propose a novel soft retargeting network (SRN) to model this specific interest. Specifically, we first calculate the similarity between target item and each historical item with the help of graph embedding. Then we learn to aggregate the similarity weights to measure the extent of user's click interest on target item. Furthermore, we model the evolution of user retargeting interest. Experimental results on public datasets and industrial dataset demonstrate that our model achieves significant improvements over state-of-the-art models.
