65 citations · 92 across the 4 of their papers we have counts for
7 papers
CL4CTR: A Contrastive Learning Framework for CTR Prediction
Fangye Wang, Yingxu Wang, Dongsheng Li +4
Many Click-Through Rate (CTR) prediction works focused on designing advanced architectures to model complex feature interactions but neglected the importance of feature representat…
Modeling Dynamic User Preference via Dictionary Learning for Sequential Recommendation
Chao Chen, Dongsheng Li, Junchi Yan +1
Capturing the dynamics in user preference is crucial to better predict user future behaviors because user preferences often drift over time. Many existing recommendation algorithms…
Leveraging Tripartite Interaction Information from Live Stream E-Commerce for Improving Product Recommendation
Sanshi Yu, Zhuoxuan Jiang, Dong-Dong Chen +4
Recently, a new form of online shopping becomes more and more popular, which combines live streaming with E-Commerce activity. The streamers introduce products and interact with th…
A Reinforcement-Learning-Based Energy-Efficient Framework for Multi-Task Video Analytics Pipeline
Yingying Zhao, Mingzhi Dong, Yujiang Wang +7
Deep-learning-based video processing has yielded transformative results in recent years. However, the video analytics pipeline is energy-intensive due to high data rates and relian…
NeuSE: A Neural Snapshot Ensemble Method for Collaborative Filtering
Dongsheng Li, Haodong Liu, Chao Chen +3
In collaborative filtering (CF) algorithms, the optimal models are usually learned by globally minimizing the empirical risks averaged over all the observed data. However, the glob…
A Scalable Algorithm for Privacy-Preserving Item-based Top-N Recommendation
Yingying Zhao, Dongsheng Li, Qin Lv +1
Recommender systems have become an indispensable component in online services during recent years. Effective recommendation is essential for improving the services of various onlin…