Abstract Knowledge Graph (KG) is widely used for recommendation tasks due to its rich
semantic information and external structure. Current knowledge graph recommendation …

Current study on next point-of-interest (POI) recommendation mainly explores user
sequential transitions with the fine-grained individual-user POI check-in trajectories only …

A recent study has shown that diffusion models are well-suited for modeling the generative
process of user--item interactions in recommender systems due to their denoising nature …

The information era brings both opportunities and challenges to information services.
Confronting information overload, recommendation technology is dedicated to filtering …

In a practical recommender system, new interactions are continuously observed. Some
interactions are expected, because they largely follow users' long-term preferences. Some …

Learning vector embeddings of users and items is the core of modern recommender
systems. Recently the collaborative filtering recommender systems based on graph …

Web Services Quality Prediction has become a popular research theme in Cloud Computing
and the Internet of Things. Graph Convolutional Network (GCN)‐based methods are more …

Recently, recommender systems have witnessed the fast evolution of Internet services.
However, it suffers hugely from inherent bias and sparsity issues in interactions. The …

Graph Convolutional Networks (GCNs) have been widely applied to collaborative filtering,
where each layer typically contains neighborhood aggregation and feature transformation …

Recommendation systems employ users history data records to predict their preference, and
have been widely used in diverse fields including biology, e-commerce, and healthcare …