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

The chronological order of user-item interactions is a key feature in many recommender
systems, where the items that users will interact may largely depend on those items that …

The chronological order of user-item interactions can reveal time-evolving and sequential
user behaviors in many recommender systems. The items that users will interact with may …

In recommender systems, top-N recommendation is an important task with implicit feedback
data. Although the recent success of deep learning largely pushes forward the research on …

Graph neural networks (GNNs) have achieved remarkable performance in personalized
recommendation, for their powerful data representation capabilities. However, these …

Personalized recommender systems are playing an increasingly important role as more
content and services become available and users struggle to identify what might interest …

A sequential recommendation has become a hot research topic, which seeks to predict the
next interesting item for each user based on his action sequence. While previous methods …

Recently, one critical issue looms large in the field of recommender systems–there are no
effective benchmarks for rigorous evaluation–which consequently leads to unreproducible …

Session-based recommendation is a challenging task dedicating to predict the user's next
item of interest based on a given sequence of anonymous behaviors. Most existing methods …

With the explosive growth of products and multimedia contents on the Internet, the desire of
users to find these online resources matching their interests makes it imperative to develop …