Abstract Background and Objectives: Transformer is a model relying entirely on self-
attention which has a wide range of applications in the field of natural language processing …

Deep learning has achieved remarkable success in numerous domains with help from large
amounts of big data. However, the quality of data labels is a concern because of the lack of …

Influenced by the great success of deep learning in computer vision and language
understanding, research in recommendation has shifted to inventing new recommender …

Click-through rate (CTR) prediction, which aims to predict the probability of a user clicking
on an ad or an item, is critical to many online applications such as online advertising and …

Combinatorial features are essential for the success of many commercial models. Manually
crafting these features usually comes with high cost due to the variety, volume and velocity …

Recommender systems are effective tools of information filtering that are prevalent due to
increasing access to the Internet, personalization trends, and changing habits of computer …

Many predictive tasks of web applications need to model categorical variables, such as user
IDs and demographics like genders and occupations. To apply standard machine learning …

In the past decade, wired and wireless computer networks have substantially evolved
because of the rapid development of technologies such as the Internet of Things (IoT) …

Learning sophisticated feature interactions behind user behaviors is critical in maximizing
CTR for recommender systems. Despite great progress, existing methods seem to have a …

In recent years, there are a large number of recommendation algorithms proposed in the
literature, from traditional collaborative filtering to deep learning algorithms. However, the …