Visual representation learning is ubiquitous in various real-world applications, including
visual comprehension, video understanding, multi-modal analysis, human-computer …

While recent years have witnessed a rapid growth of research papers on recommender
system (RS), most of the papers focus on inventing machine learning models to better fit …

As information filtering services, recommender systems have extremely enriched our daily
life by providing personalized suggestions and facilitating people in decision-making, which …

In recommender systems, the collected data used for training is always subject to selection
bias, which poses a great challenge for unbiased learning. Previous studies proposed …

Recently, recommender system (RS) based on causal inference has gained much attention
in the industrial community, as well as the states of the art performance in many prediction …

Ratings of a user to most items in recommender systems are usually missing not at random
(MNAR), largely because users are free to choose which items to rate. To achieve unbiased …

In recommender systems, users always choose the favorite items to rate, which leads to data
missing not at random and poses a great challenge for unbiased evaluation and learning of …