XCrossNet: Feature Structure-Oriented Learning for Click-Through Rate Prediction
This work addresses a core task in commercial recommender systems, offering an incremental improvement by focusing on feature structure learning for better CTR prediction.
The paper tackles the problem of click-through rate prediction by proposing XCrossNet, a model that learns dense and sparse feature interactions explicitly, leading to significant improvements in effectiveness and efficiency over state-of-the-art models on the Criteo Kaggle dataset.
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.