91 citations · 183 across the 22 of their papers we have counts for
22 papers
Selective and Collaborative Influence Function for Efficient Recommendation Unlearning
Yuyuan Li, Chaochao Chen, Xiaolin Zheng +3
Recent regulations on the Right to be Forgotten have greatly influenced the way of running a recommender system, because users now have the right to withdraw their private data. Be…
Heterogeneous Information Crossing on Graphs for Session-based Recommender Systems
Xiaolin Zheng, Rui Wu, Zhongxuan Han +3
Recommender systems are fundamental information filtering techniques to recommend content or items that meet users' personalities and potential needs. As a crucial solution to addr…
Protecting Split Learning by Potential Energy Loss
Fei Zheng, Chaochao Chen, Lingjuan Lyu +5
As a practical privacy-preserving learning method, split learning has drawn much attention in academia and industry. However, its security is constantly being questioned since the…
DDGHM: Dual Dynamic Graph with Hybrid Metric Training for Cross-Domain Sequential Recommendation
Xiaolin Zheng, Jiajie Su, Weiming Liu +1
Sequential Recommendation (SR) characterizes evolving patterns of user behaviors by modeling how users transit among items. However, the short interaction sequences limit the perfo…
Exploiting Variational Domain-Invariant User Embedding for Partially Overlapped Cross Domain Recommendation
Weiming Liu, Xiaolin Zheng, Mengling Hu +1
Cross-Domain Recommendation (CDR) has been popularly studied to utilize different domain knowledge to solve the cold-start problem in recommender systems. Most of the existing CDR…
HCFRec: Hash Collaborative Filtering via Normalized Flow with Structural Consensus for Efficient Recommendation
Fan Wang, Weiming Liu, Chaochao Chen +2
The ever-increasing data scale of user-item interactions makes it challenging for an effective and efficient recommender system. Recently, hash-based collaborative filtering (Hash-…
A Survey of Trustworthy Graph Learning: Reliability, Explainability, and Privacy Protection
Bingzhe Wu, Jintang Li, Junchi Yu +17
Deep graph learning has achieved remarkable progresses in both business and scientific areas ranging from finance and e-commerce, to drug and advanced material discovery. Despite t…
Partial Relaxed Optimal Transport for Denoised Recommendation
Yanchao Tan, Carl Yang Member, Xiangyu Wei +2
The interaction data used by recommender systems (RSs) inevitably include noises resulting from mistaken or exploratory clicks, especially under implicit feedbacks. Without proper…
Making Recommender Systems Forget: Learning and Unlearning for Erasable Recommendation
Yuyuan Li, Xiaolin Zheng, Chaochao Chen +1
Privacy laws and regulations enforce data-driven systems, e.g., recommender systems, to erase the data that concern individuals. As machine learning models potentially memorize the…
Collaborative Filtering with Attribution Alignment for Review-based Non-overlapped Cross Domain Recommendation
Weiming Liu, Xiaolin Zheng, Mengling Hu +1
Cross-Domain Recommendation (CDR) has been popularly studied to utilize different domain knowledge to solve the data sparsity and cold-start problem in recommender systems. In this…
Differential Private Knowledge Transfer for Privacy-Preserving Cross-Domain Recommendation
Chaochao Chen, Huiwen Wu, Jiajie Su +3
Cross Domain Recommendation (CDR) has been popularly studied to alleviate the cold-start and data sparsity problem commonly existed in recommender systems. CDR models can improve t…
Towards Secure and Practical Machine Learning via Secret Sharing and Random Permutation
Fei Zheng, Chaochao Chen, Xiaolin Zheng +1
With the increasing demands for privacy protection, privacy-preserving machine learning has been drawing much attention in both academia and industry. However, most existing method…
Multi-Facet Recommender Networks with Spherical Optimization
Yanchao Tan, Carl Yang, Xiangyu Wei +2
Implicit feedback is widely explored by modern recommender systems. Since the feedback is often sparse and imbalanced, it poses great challenges to the learning of complex interact…
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…
Vertically Federated Graph Neural Network for Privacy-Preserving Node Classification
Chaochao Chen, Jun Zhou, Longfei Zheng +7
Recently, Graph Neural Network (GNN) has achieved remarkable progresses in various real-world tasks on graph data, consisting of node features and the adjacent information between…
Practical Privacy Preserving POI Recommendation
Chaochao Chen, Jun Zhou, Bingzhe Wu +4
Point-of-Interest (POI) recommendation has been extensively studied and successfully applied in industry recently. However, most existing approaches build centralized models on the…
Semi-supervised Learning Meets Factorization: Learning to Recommend with Chain Graph Model
Chaochao Chen, Kevin C. Chang, Qibing Li +1
Recently latent factor model (LFM) has been drawing much attention in recommender systems due to its good performance and scalability. However, existing LFMs predict missing values…
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…
FinBrain: When Finance Meets AI 2.0
Xiaolin Zheng, Mengying Zhu, Qibing Li +2
Artificial intelligence (AI) is the core technology of technological revolution and industrial transformation. As one of the new intelligent needs in the AI 2.0 era, financial inte…
User-Sensitive Recommendation Ensemble with Clustered Multi-Task Learning
Menghan Wang, Xiaolin Zheng, Kun Zhang
This paper considers recommendation algorithm ensembles in a user-sensitive manner. Recently researchers have proposed various effective recommendation algorithms, which utilized d…
Neural Collaborative Autoencoder
Qibing Li, Xiaolin Zheng, Xinyue Wu
In recent years, deep neural networks have yielded state-of-the-art performance on several tasks. Although some recent works have focused on combining deep learning with recommenda…
Collaborative Filtering with Social Exposure: A Modular Approach to Social Recommendation
Menghan Wang, Xiaolin Zheng, Yang Yang +1
This paper is concerned with how to make efficient use of social information to improve recommendations. Most existing social recommender systems assume people share similar prefer…