Collaborative filtering (CF) is a prevalent technique utilized in recommender systems (RSs),
and has been extensively deployed in various real-world applications. A recent study in CF …

Multi-types of user behavior data (eg, clicking, adding to cart, and purchasing) are recorded
in most real-world recommendation scenarios, which can help to learn users' multi-faceted …

Graph convolutional networks (GCNs) have been widely used to learn high-quality
representations (aka embeddings) from multiorder neighbors in recommendation tasks …

Graph Convolutional Networks (GCNs) are a popular type of machine learning models that
use multiple layers of convolutional aggregation operations and non-linear activations to …

With the capacity to capture high-order collaborative signals, Graph Neural Networks
(GNNs) have emerged as powerful methods in Recommender Systems (RS). However, their …

The activities in many real-world applications, such as e-commerce and online education,
are usually modeled as a dynamic bipartite graph that evolves over time. It is a critical task to …

The success of graph neural network-based models (GNNs) has significantly advanced
recommender systems by effectively modeling users and items as a bipartite, undirected …

Loss functions steer the optimization direction of recommendation models and are critical to
model performance, but have received relatively little attention in recent recommendation …

The multimodal recommendation has gradually become the infrastructure of online media
platforms, enabling them to provide personalized service to users through a joint modeling …

Previous knowledge graph-based recommendation models use Graph Neural Networks to
aggregate information from neighbors. However, entities of the same hop should have …