Recommender systems help people find relevant content in a personalized way. One main
promise of such systems is that they are able to increase the visibility of items in the long tail …

Collaborative Filtering (CF) plays a crucial role in modern recommender systems, leveraging
historical user-item interactions to provide personalized suggestions. However, CF-based …

Graph serves as a powerful tool for modeling data that has an underlying structure in non-
Euclidean space, by encoding relations as edges and entities as nodes. Despite …

Recommender systems are deployed heavily by many online platforms for better user
engagement and providing recommendations. Despite being so popular, several works …

Continual graph learning (CGL) studies the problem of learning from an infinite stream of
graph data, consolidating historical knowledge, and generalizing it to the future task. At …

A group recommender system (GRS) generates suggestions for a group of individuals,
considering not only each person's preferences but also factors such as social dynamics …

Uncovering rationales behind predictions of graph neural networks (GNNs) has received
increasing attention over recent years. Instance-level GNN explanation aims to discover …

In many real-world scenarios, distribution shifts exist in the streaming data across time steps.
Many complex sequential data can be effectively divided into distinct regimes that exhibit …

Popularity bias is a widespread problem in the field of recommender systems, where
popular items tend to dominate recommendation results. In this work, we propose'Test Time …

Imitation learning, which learns agent policy by mimicking expert demonstration, has shown
promising results in many applications such as medical treatment regimes and self-driving …