361 citations · 499 across the 14 of their papers we have counts for
14 papers
A Counterfactual Collaborative Session-based Recommender System
Wenzhuo Song, Shoujin Wang, Yan Wang +3
Most session-based recommender systems (SBRSs) focus on extracting information from the observed items in the current session of a user to predict a next item, ignoring the causes…
Beyond CNNs: Exploiting Further Inherent Symmetries in Medical Image Segmentation
Shuchao Pang, Anan Du, Mehmet A. Orgun +5
Automatic tumor or lesion segmentation is a crucial step in medical image analysis for computer-aided diagnosis. Although the existing methods based on Convolutional Neural Network…
A Unified Framework for Cross-Domain and Cross-System Recommendations
Feng Zhu, Yan Wang, Jun Zhou +3
Cross-Domain Recommendation (CDR) and Cross-System Recommendation (CSR) have been proposed to improve the recommendation accuracy in a target dataset (domain/system) with the help…
Next-item Recommendations in Short Sessions
Wenzhuo Song, Shoujin Wang, Yan Wang +1
The changing preferences of users towards items trigger the emergence of session-based recommender systems (SBRSs), which aim to model the dynamic preferences of users for next-ite…
Graph Learning based Recommender Systems: A Review
Shoujin Wang, Liang Hu, Yan Wang +6
Recent years have witnessed the fast development of the emerging topic of Graph Learning based Recommender Systems (GLRS). GLRS employ advanced graph learning approaches to model u…
Cross-Domain Recommendation: Challenges, Progress, and Prospects
Feng Zhu, Yan Wang, Chaochao Chen +3
To address the long-standing data sparsity problem in recommender systems (RSs), cross-domain recommendation (CDR) has been proposed to leverage the relatively richer information f…
Towards Scalable and Privacy-Preserving Deep Neural Network via Algorithmic-Cryptographic Co-design
Jun Zhou, Longfei Zheng, Chaochao Chen +6
Deep Neural Networks (DNNs) have achieved remarkable progress in various real-world applications, especially when abundant training data are provided. However, data isolation has b…
Stratified and Time-aware Sampling based Adaptive Ensemble Learning for Streaming Recommendations
Yan Zhao, Shoujin Wang, Yan Wang +1
Recommender systems have played an increasingly important role in providing users with tailored suggestions based on their preferences. However, the conventional offline recommende…
Double-Wing Mixture of Experts for Streaming Recommendations
Yan Zhao, Shoujin Wang, Yan Wang +2
Streaming Recommender Systems (SRSs) commonly train recommendation models on newly received data only to address user preference drift, i.e., the changing user preferences towards…
Graph Learning Approaches to Recommender Systems: A Review
Shoujin Wang, Liang Hu, Yan Wang +7
Recent years have witnessed the fast development of the emerging topic of Graph Learning based Recommender Systems (GLRS). GLRS mainly employ the advanced graph learning approaches…
Updates-Aware Graph Pattern based Node Matching
Guohao Sun, Guanfeng Liu, Yan Wang +1
Graph Pattern based Node Matching (GPNM) is to find all the matches of the nodes in a data graph GD based on a given pattern graph GP. GPNM has become increasingly important in man…
Sequential Recommender Systems: Challenges, Progress and Prospects
Shoujin Wang, Liang Hu, Yan Wang +3
The emerging topic of sequential recommender systems has attracted increasing attention in recent years.Different from the conventional recommender systems including collaborative…
Adaptive Portfolio by Solving Multi-armed Bandit via Thompson Sampling
Mengying Zhu, Xiaolin Zheng, Yan Wang +2
As the cornerstone of modern portfolio theory, Markowitz's mean-variance optimization is considered a major model adopted in portfolio management. However, due to the difficulty of…
A Survey on Session-based Recommender Systems
Shoujin Wang, Longbing Cao, Yan Wang +3
Recommender systems (RSs) have been playing an increasingly important role for informed consumption, services, and decision-making in the overloaded information era and digitized e…