65 citations · 205 across the 14 of their papers we have counts for
14 papers
AutoSeqRec: Autoencoder for Efficient Sequential Recommendation
Sijia Liu, Jiahao Liu, Hansu Gu +4
Sequential recommendation demonstrates the capability to recommend items by modeling the sequential behavior of users. Traditional methods typically treat users as sequences of ite…
Recommendation Unlearning via Matrix Correction
Jiahao Liu, Dongsheng Li, Hansu Gu +5
Recommender systems are important for providing personalized services to users, but the vast amount of collected user data has raised concerns about privacy (e.g., sensitive data),…
Simulating News Recommendation Ecosystem for Fun and Profit
Guangping Zhang, Dongsheng Li, Hansu Gu +3
Understanding the evolution of online news communities is essential for designing more effective news recommender systems. However, due to the lack of appropriate datasets and plat…
Triple Structural Information Modelling for Accurate, Explainable and Interactive Recommendation
Jiahao Liu, Dongsheng Li, Hansu Gu +4
In dynamic interaction graphs, user-item interactions usually follow heterogeneous patterns, represented by different structural information, such as user-item co-occurrence, seque…
Personalized Graph Signal Processing for Collaborative Filtering
Jiahao Liu, Dongsheng Li, Hansu Gu +4
The collaborative filtering (CF) problem with only user-item interaction information can be solved by graph signal processing (GSP), which uses low-pass filters to smooth the obser…
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…
Parameter-free Dynamic Graph Embedding for Link Prediction
Jiahao Liu, Dongsheng Li, Hansu Gu +3
Dynamic interaction graphs have been widely adopted to model the evolution of user-item interactions over time. There are two crucial factors when modelling user preferences for li…
Enhancing CTR Prediction with Context-Aware Feature Representation Learning
Fangye Wang, Yingxu Wang, Dongsheng Li +4
CTR prediction has been widely used in the real world. Many methods model feature interaction to improve their performance. However, most methods only learn a fixed representation…
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…
Collaborative Filtering with Stability
Dongsheng Li, Chao Chen, Qin Lv +3
Collaborative filtering (CF) is a popular technique in today's recommender systems, and matrix approximation-based CF methods have achieved great success in both rating prediction…