Click-through rate (CTR) estimation plays as a core function module in various personalized
online services, including online advertising, recommender systems, and web search etc …

Research on recommender systems (RSs) has traditionally focused on the design of
systems capable of suggesting items of interest for users. However, often the most important …

Traditional industry recommendation systems usually use data in a single domain to train
models and then serve the domain. However, a large-scale commercial platform often …

Rich user behavior data has been proven to be of great value for click-through rate
prediction tasks, especially in industrial applications such as recommender systems and …

The self-attention mechanism is rapidly emerging as one of the most important key primitives
in neural networks (NNs) for its ability to identify the relations within input entities. The self …

The past two decades have witnessed the rapid development of personalized
recommendation techniques. Despite the significant progress made in both research and …

Click-through rate (CTR) prediction is a critical task for many applications, as its accuracy
has a direct impact on user experience and platform revenue. In recent years, CTR …

Feature interaction has been recognized as an important problem in machine learning,
which is also very essential for click-through rate (CTR) prediction tasks. In recent years …

Both explicit and implicit feedbacks can reflect user opinions on items, which are essential
for learning user preferences in recommendation. However, most current recommendation …

Recommender Systems have been playing essential roles in e-commerce portals. Existing
recommendation algorithms usually learn the ranking scores of items by optimizing a single …