Approximate nearest neighbor search (ANNS) constitutes an important operation in a
multitude of applications, including recommendation systems, information retrieval, and …

Knowledge graph (KG) plays an increasingly important role in recommender systems.
Recently, graph neural networks (GNNs) based model has gradually become the theme of …

Here we present a machine learning framework and model implementation that can learn to
simulate a wide variety of challenging physical domains, involving fluids, rigid solids, and …

Multimodal recommender systems utilizing multimodal features (eg, images and textual
descriptions) typically show better recommendation accuracy than general recommendation …

Multimedia content is of predominance in the modern Web era. Investigating how users
interact with multimodal items is a continuing concern within the rapid development of …

Graph Neural Networks (GNNs) have achieved tremendous success in various real-world
applications due to their strong ability in graph representation learning. GNNs explore the …

Abstract Graph Neural Networks (GNNs) are widely used for analyzing graph-structured
data. Most GNN methods are highly sensitive to the quality of graph structures and usually …

Technology development has accelerated the volume growth of complex data, such as
images, videos, time series, and georeferenced data. Similarity search is a widely used …

Multimedia recommendation has received much attention in recent years. It models user
preferences based on both behavior information and item multimodal information. Though …

K-Nearest Neighbor Graph (K-NNG) construction is an important operation with many web
related applications, including collaborative filtering, similarity search, and many others in …