XCrossNet：面向特征结构的点击率预测方法
XCrossNet: Feature Structure-Oriented Learning for Click-Through Rate Prediction
摘要：
点击率预测是当前商业推荐系统的核心任务。特征交叉作为CTR预测的研究主线，为提高预测性能提供了一条很有前途的途径。尽管各种模型能够在没有手动特征工程的情况下学习特征交互，但它们很少尝试单独学习不同特征结构的表示。特别是，他们主要集中在交叉稀疏特征的建模，但忽略了具体表示交叉密集特征。出于这一动机，我们提出了一种新的极端交叉网络，简称XCrossNet，其目的是以显式的方式学习密集和稀疏特征的相互作用。XCrossNet作为一种面向特征结构的模型，不仅具有显式的可解释性，而且具有时间效率高、易于实现的特点，从而实现了更具表现力的表示和更精确的CTR预测。在Criteo Kaggle数据集上的实验研究表明，XCrossNet在有效性和效率方面都比最先进的模型有显着提高。
英文摘要：
Click-Through Rate (CTR) prediction is a core task in nowadays commercial recommender systems. Feature crossing, as the mainline of research on CTR prediction, has shown a promising way to enhance predictive performance. Even though various models are able to learn feature interactions without manual feature engineering, they rarely attempt to individually learn representations for different feature structures. In particular, they mainly focus on the modeling of cross sparse features but neglect to specifically represent cross dense features. Motivated by this, we propose a novel Extreme Cross Network, abbreviated XCrossNet, which aims at learning dense and sparse feature interactions in an explicit manner. XCrossNet as a feature structure-oriented model leads to a more expressive representation and a more precise CTR prediction, which is not only explicit and interpretable, but also time-efficient and easy to implement. Experimental studies on Criteo Kaggle dataset show significant improvement of XCrossNet over state-of-the-art models on both effectiveness and efficiency.
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。
