1.2k citations · 1.6k across the 38 of their papers we have counts for
38 papers
Unsupervised Representation Learning for Time Series: A Review
Qianwen Meng, Hangwei Qian, Yong Liu +3
Unsupervised representation learning approaches aim to learn discriminative feature representations from unlabeled data, without the requirement of annotating every sample. Enablin…
HeteFedRec: Federated Recommender Systems with Model Heterogeneity
Wei Yuan, Liang Qu, Lizhen Cui +3
Owing to the nature of privacy protection, federated recommender systems (FedRecs) have garnered increasing interest in the realm of on-device recommender systems. However, most ex…
Explicit Knowledge Graph Reasoning for Conversational Recommendation
Xuhui Ren, Tong Chen, Quoc Viet Hung Nguyen +3
Traditional recommender systems estimate user preference on items purely based on historical interaction records, thus failing to capture fine-grained yet dynamic user interests an…
Continuous Input Embedding Size Search For Recommender Systems
Yunke Qu, Tong Chen, Xiangyu Zhao +3
Latent factor models are the most popular backbones for today's recommender systems owing to their prominent performance. Latent factor models represent users and items as real-val…
FedMAE: Federated Self-Supervised Learning with One-Block Masked Auto-Encoder
Nan Yang, Xuanyu Chen, Charles Z. Liu +3
Latest federated learning (FL) methods started to focus on how to use unlabeled data in clients for training due to users' privacy concerns, high labeling costs, or lack of experti…
Dual Graph Multitask Framework for Imbalanced Delivery Time Estimation
Lei Zhang, Mingliang Wang, Xin Zhou +5
Delivery Time Estimation (DTE) is a crucial component of the e-commerce supply chain that predicts delivery time based on merchant information, sending address, receiving address,…
Interaction-level Membership Inference Attack Against Federated Recommender Systems
Wei Yuan, Chaoqun Yang, Quoc Viet Hung Nguyen +3
The marriage of federated learning and recommender system (FedRec) has been widely used to address the growing data privacy concerns in personalized recommendation services. In Fed…
A Survey on Federated Recommendation Systems
Zehua Sun, Yonghui Xu, Yong Liu +5
Federated learning has recently been applied to recommendation systems to protect user privacy. In federated learning settings, recommendation systems can train recommendation mode…
Towards AI-Empowered Crowdsourcing
Shipeng Wang, Qingzhong Li, Lizhen Cui +6
Crowdsourcing, in which human intelligence and productivity is dynamically mobilized to tackle tasks too complex for automation alone to handle, has grown to be an important resear…
MHCCL: Masked Hierarchical Cluster-Wise Contrastive Learning for Multivariate Time Series
Qianwen Meng, Hangwei Qian, Yong Liu +3
Learning semantic-rich representations from raw unlabeled time series data is critical for downstream tasks such as classification and forecasting. Contrastive learning has recentl…
Utility Maximizer or Value Maximizer: Mechanism Design for Mixed Bidders in Online Advertising
Hongtao Lv, Zhilin Zhang, Zhenzhe Zheng +5
Digital advertising constitutes one of the main revenue sources for online platforms. In recent years, some advertisers tend to adopt auto-bidding tools to facilitate advertising p…
XSimGCL: Towards Extremely Simple Graph Contrastive Learning for Recommendation
Junliang Yu, Xin Xia, Tong Chen +3
Contrastive learning (CL) has recently been demonstrated critical in improving recommendation performance. The underlying principle of CL-based recommendation models is to ensure t…
Enhancing Sequential Recommendation with Graph Contrastive Learning
Yixin Zhang, Yong Liu, Yonghui Xu +5
The sequential recommendation systems capture users' dynamic behavior patterns to predict their next interaction behaviors. Most existing sequential recommendation methods only exp…
Heterogeneous Collaborative Learning for Personalized Healthcare Analytics via Messenger Distillation
Guanhua Ye, Tong Chen, Yawen Li +3
In this paper, we propose a Similarity-Quality-based Messenger Distillation (SQMD) framework for heterogeneous asynchronous on-device healthcare analytics. By introducing a preload…
Unified Question Generation with Continual Lifelong Learning
Wei Yuan, Hongzhi Yin, Tieke He +3
Question Generation (QG), as a challenging Natural Language Processing task, aims at generating questions based on given answers and context. Existing QG methods mainly focus on bu…
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…