1.2k citations · 1.6k across the 38 of their papers we have counts for
17 papers · 1 filter
Are Graph Augmentations Necessary? Simple Graph Contrastive Learning for Recommendation
Junliang Yu, Hongzhi Yin, Xin Xia +3
Contrastive learning (CL) recently has spurred a fruitful line of research in the field of recommendation, since its ability to extract self-supervised signals from the raw data is…
Cross-modal Zero-shot Hashing by Label Attributes Embedding
Runmin Wang, Guoxian Yu, Lei Liu +3
Cross-modal hashing (CMH) is one of the most promising methods in cross-modal approximate nearest neighbor search. Most CMH solutions ideally assume the labels of training and test…
Open-Set Crowdsourcing using Multiple-Source Transfer Learning
Guangyang Han, Guoxian Yu, Lei Liu +3
We raise and define a new crowdsourcing scenario, open set crowdsourcing, where we only know the general theme of an unfamiliar crowdsourcing project, and we don't know its label s…
Crowdsourcing with Meta-Workers: A New Way to Save the Budget
Guangyang Han, Guoxian Yu, Lizhen Cui +2
Due to the unreliability of Internet workers, it's difficult to complete a crowdsourcing project satisfactorily, especially when the tasks are multiple and the budget is limited. R…
Major Depressive Disorder Recognition and Cognitive Analysis Based on Multi-layer Brain Functional Connectivity Networks
Xiaofang Sun, Xiangwei Zheng, Yonghui Xu +2
On the increase of major depressive disorders (MDD), many researchers paid attention to their recognition and treatment. Existing MDD recognition algorithms always use a single tim…
PipAttack: Poisoning Federated Recommender Systems forManipulating Item Promotion
Shijie Zhang, Hongzhi Yin, Tong Chen +3
Due to the growing privacy concerns, decentralization emerges rapidly in personalized services, especially recommendation. Also, recent studies have shown that centralized models a…
GTG-Shapley: Efficient and Accurate Participant Contribution Evaluation in Federated Learning
Zelei Liu, Yuanyuan Chen, Han Yu +2
Federated Learning (FL) bridges the gap between collaborative machine learning and preserving data privacy. To sustain the long-term operation of an FL ecosystem, it is important t…
Self-Supervised Graph Co-Training for Session-based Recommendation
Xin Xia, Hongzhi Yin, Junliang Yu +2
Session-based recommendation targets next-item prediction by exploiting user behaviors within a short time period. Compared with other recommendation paradigms, session-based recom…
Hyperbolic Hypergraphs for Sequential Recommendation
Yicong Li, Hongxu Chen, Xiangguo Sun +5
Hypergraphs have been becoming a popular choice to model complex, non-pairwise, and higher-order interactions for recommender system. However, compared with traditional graph-based…
Noise-Resistant Deep Metric Learning with Probabilistic Instance Filtering
Chang Liu, Han Yu, Boyang Li +6
Noisy labels are commonly found in real-world data, which cause performance degradation of deep neural networks. Cleaning data manually is labour-intensive and time-consuming. Prev…
Few-Shot Partial-Label Learning
Yunfeng Zhao, Guoxian Yu, Lei Liu +3
Partial-label learning (PLL) generally focuses on inducing a noise-tolerant multi-class classifier by training on overly-annotated samples, each of which is annotated with a set of…
Where are we in embedding spaces? A Comprehensive Analysis on Network Embedding Approaches for Recommender Systems
Sixiao Zhang, Hongxu Chen, Xiao Ming +3
Hyperbolic space and hyperbolic embeddings are becoming a popular research field for recommender systems. However, it is not clear under what circumstances the hyperbolic space sho…
NoiLIn: Improving Adversarial Training and Correcting Stereotype of Noisy Labels
Jingfeng Zhang, Xilie Xu, Bo Han +4
Adversarial training (AT) formulated as the minimax optimization problem can effectively enhance the model's robustness against adversarial attacks. The existing AT methods mainly…
Noise-resistant Deep Metric Learning with Ranking-based Instance Selection
Chang Liu, Han Yu, Boyang Li +6
The existence of noisy labels in real-world data negatively impacts the performance of deep learning models. Although much research effort has been devoted to improving robustness…
Towards Personalized Federated Learning
Alysa Ziying Tan, Han Yu, Lizhen Cui +1
In parallel with the rapid adoption of Artificial Intelligence (AI) empowered by advances in AI research, there have been growing awareness and concerns of data privacy. Recent sig…
Graph Embedding for Recommendation against Attribute Inference Attacks
Shijie Zhang, Hongzhi Yin, Tong Chen +3
In recent years, recommender systems play a pivotal role in helping users identify the most suitable items that satisfy personal preferences. As user-item interactions can be natur…
FENet: A Frequency Extraction Network for Obstructive Sleep Apnea Detection
Guanhua Ye, Hongzhi Yin, Tong Chen +3
Obstructive Sleep Apnea (OSA) is a highly prevalent but inconspicuous disease that seriously jeopardizes the health of human beings. Polysomnography (PSG), the gold standard of det…