5 citations · 6 across the 12 of their papers we have counts for
8 papers · 1 filter
Watermarking Large Language Model-based Time Series Forecasting
Wei Yuan, Chaoqun Yang, Yu Xing +3
Large Language Model-based Time Series Forecasting (LLMTS) has shown remarkable promise in handling complex and diverse temporal data, representing a significant step toward founda…
FLDmamba: Integrating Fourier and Laplace Transform Decomposition with Mamba for Enhanced Time Series Prediction
Qianru Zhang, Chenglei Yu, Haixin Wang +5
Time series prediction, a crucial task across various domains, faces significant challenges due to the inherent complexities of time series data, including non-stationarity, multi-…
On Mitigating Data Sparsity in Conversational Recommender Systems
Sixiao Zhang, Mingrui Liu, Wei Yuan +2
Conversational recommender systems (CRSs) infer user preferences from dialogue contexts, but they suffer from severe data sparsity in both dialogue and entity spaces. Dialogue data…
HMamba: Hyperbolic Mamba for Sequential Recommendation
Qianru Zhang, Honggang Wen, Wei Yuan +4
Sequential recommendation systems have become a cornerstone of personalized services, adept at modeling the temporal evolution of user preferences by capturing dynamic interaction…
M2Rec: Multi-scale Mamba for Efficient Sequential Recommendation
Qianru Zhang, Liang Qu, Honggang Wen +4
Sequential recommendation systems aim to predict users' next preferences based on their interaction histories, but existing approaches face critical limitations in efficiency and m…
Multi-agents based User Values Mining for Recommendation
Lijian Chen, Wei Yuan, Tong Chen +3
Recommender systems have rapidly evolved and become integral to many online services. However, existing systems sometimes produce unstable and unsatisfactory recommendations that f…
Time Series Analysis in Frequency Domain: A Survey of Open Challenges, Opportunities and Benchmarks
Qianru Zhang, Yuting Sun, Honggang Wen +6
Frequency-domain analysis has emerged as a powerful paradigm for time series analysis, offering unique advantages over traditional time-domain approaches while introducing new theo…
Efficient Traffic Prediction Through Spatio-Temporal Distillation
Qianru Zhang, Xinyi Gao, Haixin Wang +2
Graph neural networks (GNNs) have gained considerable attention in recent years for traffic flow prediction due to their ability to learn spatio-temporal pattern representations th…