Collaborative filtering is one of the most fundamental topics for recommender systems.
Various methods have been proposed for collaborative filtering, ranging from matrix …

The collaborative filtering (CF) problem with only user-item interaction information can be
solved by graph signal processing (GSP), which uses low-pass filters to smooth the …

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

Recent studies in multivariate time series (MTS) forecasting reveal that explicitly modeling
the hidden dependencies among different time series can yield promising forecasting …

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

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) …

Temporal recommendation methods can achieve superior accuracy due to updating
user/item embeddings continuously once obtaining new interactions. However, 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 …

Over the past few years, graph neural networks (GNNs) have become powerful and practical
tools for learning on (static) graph-structure data. However, many real-world applications …

Cross-domain recommendation (CDR) extends conventional recommender systems by
leveraging user-item interactions from dense domains to mitigate data sparsity and the cold …