Text-attributed graphs (TAGs) are prevalent in various real-world scenarios, where each
node is associated with a text description. The cornerstone of representation learning on …

Sequential recommendation (SR) aims to model users' dynamic preferences from a series of
interactions. A pivotal challenge in user modeling for SR lies in the inherent variability of …

Graph neural network-based collaborative filtering methods have achieved excellent
performance in recommender systems. However, previous works have primarily focused on …

Machine learning has demonstrated remarkable performance over finite datasets, yet
whether the scores over the fixed benchmarks can sufficiently indicate the model's …

Cross-Domain Recommendation (CDR) has been popularly studied for solving the data
sparsity problem via leveraging rich knowledge from the auxiliary domain. Most of the …

With the rise of multimedia content on the internet, Multimodal Summarization has become a
challenging task to help individuals grasp vital information fast. However, previous methods …

We investigate node representation learning on text-attributed graphs (TAGs), where nodes
are associated with text information. Although recent studies on graph neural networks …

Cross-domain recommendation methods are designed to learn and transfer knowledge
across multiple domains to enhance recommendation performance, thereby offering an …

To alleviate the long-standing data sparsity issue in recommender systems, numerous
studies in cross-domain recommendation (CDR) have been conducted to facilitate …

Building high-quality recommender systems is challenging for new services and small
companies, because of their sparse interactions. Cross-domain recommendations (CDRs) …