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

Recent research in drug discovery dealing with many faces difficulties, including
development of new drugs during disease outbreak and drug resistance due to rapidly …

Cold-start problem is still a very challenging problem in recommender systems. Fortunately,
the interactions of the cold-start users in the auxiliary source domain can help cold-start …

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

We propose TabTransformer, a novel deep tabular data modeling architecture for
supervised and semi-supervised learning. The TabTransformer is built upon self-attention …

Sequential recommender systems seek to exploit the order of users' interactions, in order to
predict their next action based on the context of what they have done recently. Traditionally …

Performing highly accurate representation learning on a high-dimensional and sparse
(HiDS) matrix is of great significance in a big data-related application such as a …

Much of the recent progress in sequential and session-based recommendation has been
driven by improvements in model architecture and pretraining techniques originating in the …

Click-through rate (CTR) prediction is one of the fundamental tasks in online advertising and
recommendation. Multi-layer perceptron (MLP) serves as a core component in many deep …

Sequential dynamics are a key feature of many modern recommender systems, which seek
to capture the'context'of users' activities on the basis of actions they have performed recently …