41 citations · 163 across the 57 of their papers we have counts for
6 papers · 1 filter
Towards Automatic Sampling of User Behaviors for Sequential Recommender Systems
Hao Zhang, Mingyue Cheng, Zhiding Liu +1
Sequential recommender systems (SRS) have gained increasing popularity due to their remarkable proficiency in capturing dynamic user preferences. In the current setup of SRS, a com…
Reformulating Sequential Recommendation: Learning Dynamic User Interest with Content-enriched Language Modeling
Junzhe Jiang, Shang Qu, Mingyue Cheng +8
Recommender systems are indispensable in the realm of online applications, and sequential recommendation has enjoyed considerable prevalence due to its capacity to encapsulate the…
Exploring Adapter-based Transfer Learning for Recommender Systems: Empirical Studies and Practical Insights
Junchen Fu, Fajie Yuan, Yu Song +6
Adapters, a plug-in neural network module with some tunable parameters, have emerged as a parameter-efficient transfer learning technique for adapting pre-trained models to downstr…
TimeMAE: Self-Supervised Representations of Time Series with Decoupled Masked Autoencoders
Mingyue Cheng, Xiaoyu Tao, Zhiding Liu +4
Learning transferable representations from unlabeled time series is crucial for improving performance in data-scarce classification. Existing self-supervised methods often operate…
FormerTime: Hierarchical Multi-Scale Representations for Multivariate Time Series Classification
Mingyue Cheng, Qi Liu, Zhiding Liu +3
Deep learning-based algorithms, e.g., convolutional networks, have significantly facilitated multivariate time series classification (MTSC) task. Nevertheless, they suffer from the…
ShapeWordNet: An Interpretable Shapelet Neural Network for Physiological Signal Classification
Wenqiang He, Mingyue Cheng, Qi Liu +1
Physiological signals are high-dimensional time series of great practical values in medical and healthcare applications. However, previous works on its classification fail to obtai…