Click-through rate (CTR) prediction model usually consists of three components: embedding
table, feature interaction layer, and classifier. Learning embedding table plays a …

Click-through prediction (CTR) models transform features into latent vectors and enumerate
possible feature interactions to improve performance based on the input feature set …

Multi-scenario recommender systems (MSRSs) have been increasingly used in real-world
industrial platforms for their excellent advantages in mitigating data sparsity and reducing …

Model selection is a strategy aimed at creating accurate and robust models. A key challenge
in designing these algorithms is identifying the optimal model for classifying any particular …

Accurately predicting stock returns is crucial for effective portfolio management. However,
existing methods often overlook a fundamental issue in the market, namely, distribution …

Matrix factorization (MF) is a widely used collaborative filtering (CF) algorithm for
recommendation systems (RSs), due to its high prediction accuracy, great flexibility and high …

This paper presents a model-agnostic ensemble approach for supervised learning. The
proposed approach is based on a parametric version of Random Subspace, in which each …

Feature selection has drawn much attention over the last decades in machine learning
because it can reduce data dimensionality while maintaining the original physical meaning …

The modeling of natural processes relies on a physical description that prescribes the
changes in the state of the studied system. The use of domain specific knowledge about the …