XCrossNet: Feature Structure-Oriented Learning for Click-Through Rate Prediction

TL;DR

XCrossNet is a novel CTR prediction model that explicitly learns and represents interactions between dense and sparse features, improving accuracy and efficiency in recommender systems.

Contribution

The paper introduces XCrossNet, a feature structure-oriented model that separately learns dense and sparse feature interactions for more accurate CTR prediction.

Findings

XCrossNet outperforms state-of-the-art models on Criteo dataset.

It offers explicit, interpretable, and time-efficient feature interaction modeling.

Experimental results demonstrate significant improvements in effectiveness and efficiency.

Abstract

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.