52 citations · 73 across the 9 of their papers we have counts for
9 papers
CoRCi: Cross-Reconstruction of Coherent Interests Modeling in Cross-Domain Sequential Recommendation
Qingtian Bian, Tieying Li, Marcus de Carvalho +3
Cross-Domain Sequential Recommendation (CDSR) aims to alleviate data sparsity by transferring dynamic user interests across related domains. A key challenge lies in effectively bri…
Context-Aware Disentanglement for Cross-Domain Sequential Recommendation: A Causal View
Xingzi Wang, Qingtian Bian, Hui Fang
Cross-Domain Sequential Recommendation (CDSR) aims to en-hance recommendation quality by transferring knowledge across domains, offering effective solutions to data sparsity and co…
ABXI: Invariant Interest Adaptation for Task-Guided Cross-Domain Sequential Recommendation
Qingtian Bian, Marcus Vinícius de Carvalho, Tieying Li +3
Cross-Domain Sequential Recommendation (CDSR) has recently gained attention for countering data sparsity by transferring knowledge across domains. A common approach merges domain-s…
BrainOOD: Out-of-distribution Generalizable Brain Network Analysis
Jiaxing Xu, Yongqiang Chen, Xia Dong +5
In neuroscience, identifying distinct patterns linked to neurological disorders, such as Alzheimer's and Autism, is critical for early diagnosis and effective intervention. Graph N…
Multi-Atlas Brain Network Classification through Consistency Distillation and Complementary Information Fusion
Jiaxing Xu, Mengcheng Lan, Xia Dong +4
In the realm of neuroscience, identifying distinctive patterns associated with neurological disorders via brain networks is crucial. Resting-state functional magnetic resonance ima…
Contrasformer: A Brain Network Contrastive Transformer for Neurodegenerative Condition Identification
Jiaxing Xu, Kai He, Mengcheng Lan +5
Understanding neurological disorder is a fundamental problem in neuroscience, which often requires the analysis of brain networks derived from functional magnetic resonance imaging…
CPMR: Context-Aware Incremental Sequential Recommendation with Pseudo-Multi-Task Learning
Qingtian Bian, Jiaxing Xu, Hui Fang +1
The motivations of users to make interactions can be divided into static preference and dynamic interest. To accurately model user representations over time, recent studies in sequ…
Contrastive Graph Pooling for Explainable Classification of Brain Networks
Jiaxing Xu, Qingtian Bian, Xinhang Li +6
Functional magnetic resonance imaging (fMRI) is a commonly used technique to measure neural activation. Its application has been particularly important in identifying underlying ne…
Union Subgraph Neural Networks
Jiaxing Xu, Aihu Zhang, Qingtian Bian +2
Graph Neural Networks (GNNs) are widely used for graph representation learning in many application domains. The expressiveness of vanilla GNNs is upper-bounded by 1-dimensional Wei…