activity
20202022
most citedLearning Multi-granularity User Intent Unit for Session-based Recommendation

85 citations · 166 across the 6 of their papers we have counts for

collaborators

7 papers

q-bio.NC20225 cited

Learning Task-Aware Effective Brain Connectivity for fMRI Analysis with Graph Neural Networks

Yue Yu, Xuan Kan, Hejie Cui +9

Functional magnetic resonance imaging (fMRI) has become one of the most common imaging modalities for brain function analysis. Recently, graph neural networks (GNN) have been adopt…

cs.CL202231 cited

TaxoEnrich: Self-Supervised Taxonomy Completion via Structure-Semantic Representations

Minhao Jiang, Xiangchen Song, Jieyu Zhang +1

Taxonomies are fundamental to many real-world applications in various domains, serving as structural representations of knowledge. To deal with the increasing volume of new concept…

cs.IR202285 cited

Learning Multi-granularity User Intent Unit for Session-based Recommendation

Jiayan Guo, Yaming Yang, Xiangchen Song +4

Session-based recommendation aims to predict a user's next action based on previous actions in the current session. The major challenge is to capture authentic and complete user pr…

cs.CL20215 cited

Who Should Go First? A Self-Supervised Concept Sorting Model for Improving Taxonomy Expansion

Xiangchen Song, Jiaming Shen, Jieyu Zhang +1

Taxonomies have been widely used in various machine learning and text mining systems to organize knowledge and facilitate downstream tasks. One critical challenge is that, as data…

cs.CL20212 cited

Taxonomy Completion via Triplet Matching Network

Jieyu Zhang, Xiangchen Song, Ying Zeng +4

Automatically constructing taxonomy finds many applications in e-commerce and web search. One critical challenge is as data and business scope grow in real applications, new concep…

cs.LG2020

BiTe-GCN: A New GCN Architecture via BidirectionalConvolution of Topology and Features on Text-Rich Networks

Di Jin, Xiangchen Song, Zhizhi Yu +4

Graph convolutional networks (GCNs), aiming to integrate high-order neighborhood information through stacked graph convolution layers, have demonstrated remarkable power in many ne…