  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.
