Graph neural networks (GNNs) have shown state-of-the-art performances in various
applications. However, GNNs often struggle to capture long-range dependencies in graphs …

Sequential recommendation aims at understanding user preference by capturing successive
behavior correlations, which are usually represented as the item purchasing sequences …

In an era of information explosion, recommender systems are vital tools to deliver
personalized recommendations for users. The key of recommender systems is to forecast …

With the rapid development of artificial intelligence, there is an increasing number of
industries relying on the accuracy and efficiency of deep learning algorithms. But due to the …

Personal data have become a key input in internet commerce, facilitating the matching
between millions of customers and merchants. Recent data regulations in China, Europe …

We aim to deepen the theoretical understanding of Graph Neural Networks (GNNs) on large
graphs, with a focus on their expressive power. Existing analyses relate this notion to the …

In the era of remote sensing (RS) big data, in order to alleviate the time cost of acquiring RS
images, recommending RS images that meet users' individual needs continues to be an …

Product recommendation and search are two technology-mediated channels through which
e-commerce platforms can help customers find products. However, the relationship between …

Contrastive learning has recently emerged as an effective strategy for improving the
performance of sequential recommendation. However, traditional models commonly …

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