Predicting the probability that a user will click on a specific advertisement has been a
prevalent issue in online advertising, attracting much research attention in the past decades …

The ongoing rapid expansion of the Internet greatly increases the necessity of effective
recommender systems for filtering the abundant information. Extensive research for …

Sequential recommendation aims to leverage users' historical behaviors to predict their next
interaction. Existing works have not yet addressed two main challenges in sequential …

The research field of Information Retrieval (IR) has evolved significantly, expanding beyond
traditional search to meet diverse user information needs. Recently, Large Language …

Recently, neural networks have been widely used in e-commerce recommender systems,
owing to the rapid development of deep learning. We formalize the recommender system as …

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 …

Modeling user's long-term and short-term interests is crucial for accurate recommendation.
However, since there is no manually annotated label for user interests, existing approaches …

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 …

Recommender systems deployed in real-world applications can have inherent exposure
bias, which leads to the biased logged data plaguing the researchers. A fundamental way to …

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