Graph neural networks (GNNs) have become powerful tools for processing graph-based
information in various domains. A desirable property of GNNs is transferability, where a …

The existing graph structure learning methods heavily rely on the original graph structure
and often fail to capture potential high-level abstract features and global correlations within …

As online music platforms continue to grow, music recommender systems play a vital role in
helping users navigate and discover content within their vast musical databases. At odds …

In the realm of modern recommender systems, user-item interaction data often exhibit
sequential patterns in relation to various behaviors, such as clicks and purchases on e …

Multimodal recommender systems have acquired applications in broad web scenarios such
as e-commerce businesses and short-video platforms. Existing multimodal recommendation …

Rating prediction is crucial in recommender systems as it enables personalized
recommendations based on different models and techniques, making it of significant …

Graph neural networks (GNNs) have emerged as a series of competent graph learning
methods for diverse real-world scenarios, ranging from daily applications such as …

Line drawings by artists capture the organization, relationships, and semantics of
observable objects. To endow machines with similar capacities and improve the storage and …

In this paper we propose an aspect-based recommendation model based on variational
autoencoders, that provides not only coarse predictions about what items users may like, but …

Abstract Graph Structure Learning (GSL) has demonstrated considerable potential in the
analysis of graph-unknown non-Euclidean data across a wide range of domains. However …