5 citations · 5 across the 2 of their papers we have counts for
3 papers
cs.LG2022
Learning Large-scale Universal User Representation with Sparse Mixture of Experts
Caigao Jiang, Siqiao Xue, James Zhang +3
Learning user sequence behaviour embedding is very sophisticated and challenging due to the complicated feature interactions over time and high dimensions of user features. Recent…
cs.LG2021★ 5 cited
MixSeq: Connecting Macroscopic Time Series Forecasting with Microscopic Time Series Data
Zhibo Zhu, Ziqi Liu, Ge Jin +4
Time series forecasting is widely used in business intelligence, e.g., forecast stock market price, sales, and help the analysis of data trend. Most time series of interest are mac…
cs.IR2018
Attentive Aspect Modeling for Review-aware Recommendation
Xinyu Guan, Zhiyong Cheng, Xiangnan He +4
In recent years, many studies extract aspects from user reviews and integrate them with ratings for improving the recommendation performance. The common aspects mentioned in a user…