Scikit-Rank: Scikit-learn-Compatible Neural Ranking Models for Tabular Recommender Systems

Abstract

Deep & Cross Networks (DCN) are established deep learning architectures for modeling feature interactions in CTR prediction, ranking, and recommender systems. Although they can achieve metrics comparable to gradient-boosting methods, existing implementations are often organized around framework-specific training loops rather than estimator-style interfaces, such as XGBoost, LightGBM, and CatBoost, thereby limiting their interoperability with tabular workflows for preprocessing, cross-validation, and hyperparameter optimization. To address these limitations, we present Scikit-Rank, an open-source library that exposes DCN models via a scikit-learn-compatible API and is designed to support additional neural ranking architectures. Scikit-Rank provides configurable numerical and categorical feature encoders, as well as more than 20 pointwise, pairwise, listwise, ordinal, and composite learning objectives. Furthermore, in our experiments, encoder and objective choices improved the target evaluation metrics by up to 2% relative to reproduced DCN evaluation baselines while decreasing the number of trainable parameters without degrading predictive quality.