1.2k citations · 1.6k across the 38 of their papers we have counts for
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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…