In recent years, neural architecture-based recommender systems have achieved
tremendous success, but they still fall short of expectation when dealing with highly sparse …

Recommender systems play a significant role in information filtering and have been utilized
in different scenarios, such as e-commerce and social media. With the prosperity of deep …

Session-based recommendation (SBR) focuses on next-item prediction at a certain time
point. As user profiles are generally not available in this scenario, capturing the user intent …

Social relations are often used to improve recommendation quality when user-item
interaction data is sparse in recommender systems. Most existing social recommendation …

The emergence of Artificial Intelligence of Things (AIoT) has provided novel insights for
many social computing applications, such as group recommender systems. As the distances …

Social recommendation task aims to predict users' preferences over items with the
incorporation of social connections among users, so as to alleviate the sparse issue of …

Group recommendation has recently drawn a lot of attention to the recommender system
community. Currently, several deep learning-based approaches are leveraged to learn …

With the prevalence of social media, there has recently been a proliferation of
recommenders that shift their focus from individual modeling to group recommendation …

In recent years, recommender system has become an indispensable function in all e-
commerce platforms. The review rating data for a recommender system typically comes from …

In the mobile Internet era, recommender systems have become an irreplaceable tool to help
users discover useful items, thus alleviating the information overload problem. Recent …