Temporal graph learning aims to generate high-quality representations for graph-based
tasks with dynamic information, which has recently garnered increasing attention. In contrast …

Capsule networks (CapsNets) have been known difficult to develop a deeper architecture,
which is desirable for high performance in the deep learning era, due to the complex …

Sequential recommendation is one of the hot research topics in recent years. Various
sequential recommendation models have been proposed, of which Self-Attention (SA) …

A sequential recommendation has become a hot research topic, which seeks to predict the
next interesting item for each user based on his action sequence. While previous methods …

Recommender systems (RS) play a vital role in daily life due to their practical significance.
As a branch of RS, the sequential recommendation has attracted much attention because of …

Identifying interactions between long non-coding RNAs (lncRNAs) and microRNAs
(miRNAs) reveals the mechanisms of biological processes, thereby contributing to disease …

Recommender systems are powerful tools that successfully apply data mining and machine
learning techniques. Traditionally, these systems focused on predicting a single interaction …

Abstract Graph Neural Network (GNN) is a promising technique in representation learning
on graph data. Graph Convolution Network (GCN) and Graph Attention Network (GAT) are …

Recommendation systems provide personalized service to users and aim at suggesting to
them items that they may prefer. There is an increasing requirement of next-item …

Recent Transformer-based architectures have achieved encouraging performance for
sequential recommendation, whereas their computational complexity is quadratic to the …