The problem of data sparsity has long been a challenge in recommendation systems, and
previous studies have attempted to address this issue by incorporating side information …

The online emergence of multi-modal sharing platforms (eg, TikTok, Youtube) is powering
personalized recommender systems to incorporate various modalities (eg, visual, textual …

Recent studies show that graph neural networks (GNNs) are prevalent to model high-order
relationships for collaborative filtering (CF). Towards this research line, graph contrastive …

Graphs have a superior ability to represent relational data, such as chemical compounds,
proteins, and social networks. Hence, graph-level learning, which takes a set of graphs as …

Heterogeneous graph learning aims to capture complex relationships and diverse relational
semantics among entities in a heterogeneous graph to obtain meaningful representations …

Recommender systems play a crucial role in tackling the challenge of information overload
by delivering personalized recommendations based on individual user preferences. Deep …

Graph neural networks (GNNs) have shown the power in representation learning over graph-
structured user-item interaction data for collaborative filtering (CF) task. However, with their …

Deep learning-based recommender systems have achieved remarkable success in recent
years. However, these methods usually heavily rely on labeled data (ie, user-item …

In the era of big data and Artificial Intelligence, an emerging paradigm is to utilize contrastive
self-supervised learning to model large-scale heterogeneous data. Many existing foundation …

Personalized recommender systems play a crucial role in capturing users' evolving
preferences over time to provide accurate and effective recommendations on various online …