Recommender system is one of the most important information services on today's Internet.
Recently, graph neural networks have become the new state-of-the-art approach to …

Recommender systems have become crucial in information filtering nowadays. Existing
recommender systems extract user preferences based on the correlation in data, such as …

The existing literature on deep learning for tabular data proposes a wide range of novel
architectures and reports competitive results on various datasets. However, the proposed …

Dynamic neural network is an emerging research topic in deep learning. Compared to static
models which have fixed computational graphs and parameters at the inference stage …

Recently, deep neural networks such as RNN, CNN and Transformer have been applied in
the task of sequential recommendation, which aims to capture the dynamic preference …

Recently, significant progress has been made in sequential recommendation with deep
learning. Existing neural sequential recommendation models usually rely on the item …

The recent release of large-scale healthcare datasets has greatly propelled the research of
data-driven deep learning models for healthcare applications. However, due to the nature of …

Learning effective feature crosses is the key behind building recommender systems.
However, the sparse and large feature space requires exhaustive search to identify effective …

Recommender systems play a vital role in various online services. However, the insulated
nature of training and deploying separately within a specific domain limits their access to …

We propose a novel high-performance and interpretable canonical deep tabular data
learning architecture, TabNet. TabNet uses sequential attention to choose which features to …