Sequential recommendation demonstrates the capability to recommend items by modeling
the sequential behavior of users. Traditional methods typically treat users as sequences of …

The success of graph neural network-based models (GNNs) has significantly advanced
recommender systems by effectively modeling users and items as a bipartite, undirected …

In dynamic interaction graphs, user-item interactions usually follow heterogeneous patterns,
represented by different structural information, such as user-item co-occurrence, sequential …

In the age of information overload, modern recommendation systems provide an important
role in helping people screen massive information. With the development of deep learning …

Recommender systems are important for providing personalized services to users, but the
vast amount of collected user data has raised concerns about privacy (eg, sensitive data) …

A series of graph filtering (GF)-based collaborative filtering (CF) showcases state-of-the-art
performance on the recommendation accuracy by using a low-pass filter (LPF) without a …

Collaborative Filtering (CF) is a pivotal research area in recommender systems that
capitalizes on collaborative similarities between users and items to provide personalized …

Graph Signal Processing (GSP) based recommendation algorithms have recently attracted
lots of attention due to its high efficiency. However, these methods failed to consider the …

Graph Signal Processing (GSP) has proven to be a highly effective and efficient tool for
predicting user future interactions in recommender systems. However, current GSP methods …

The rapid evolution of the web has led to an exponential growth in content. Recommender
systems play a crucial role in human–computer interaction (HCI) by tailoring content based …