The Knowledge graph, a multi-relational graph that represents rich factual information
among entities of diverse classifications, has gradually become one of the critical tools for …

Real-world problems often exhibit complex relationships and dependencies, which can be
effectively captured by graph learning systems. Graph attention networks (GATs) have …

A well-informed recommendation framework could not only help users identify their
interested items, but also benefit the revenue of various online platforms (eg, e-commerce …

Applying a graph convolutional network (GCN) or its variants to user-item interaction graphs
is one of the most commonly used approaches for learning the representation of users and …

Cross-domain recommendation aims to leverage knowledge from multiple domains to
alleviate the data sparsity and cold-start problems in traditional recommender systems. One …

Motivation Graphs or networks are widely utilized to model the interactions between different
entities (eg proteins, drugs, etc.) for biomedical applications. Predicting potential …

Side information of items, eg, images and text description, has shown to be effective in
contributing to accurate recommendations. Inspired by the recent success of pre-training …

Existing works in recommender system have widely explored extracting reviews as
explanations beyond user–item interactions, and formulated the explanation generation as a …

In online learning scenarios, the learners usually hope to find courses that meet their
preferences and the needs for their future developments. Thus, there is a great need to …

In this work, we present TriMLP as a foundational MLP-like architecture for the sequential
recommendation, simultaneously achieving computational efficiency and promising …