47 citations · 314 across the 34 of their papers we have counts for
34 papers
Making Users Indistinguishable: Attribute-wise Unlearning in Recommender Systems
Yuyuan Li, Chaochao Chen, Xiaolin Zheng +4
With the growing privacy concerns in recommender systems, recommendation unlearning, i.e., forgetting the impact of specific learned targets, is getting increasing attention. Exist…
In-processing User Constrained Dominant Sets for User-Oriented Fairness in Recommender Systems
Zhongxuan Han, Chaochao Chen, Xiaolin Zheng +4
Recommender systems are typically biased toward a small group of users, leading to severe unfairness in recommendation performance, i.e., User-Oriented Fairness (UOF) issue. The ex…
Integration of Large Language Models and Federated Learning
Chaochao Chen, Xiaohua Feng, Yuyuan Li +4
As the parameter size of Large Language Models (LLMs) continues to expand, there is an urgent need to address the scarcity of high-quality data. In response, existing research has…
Class-wise Federated Unlearning: Harnessing Active Forgetting with Teacher-Student Memory Generation
Yuyuan Li, Jiaming Zhang, Yixiu Liu +1
Privacy concerns associated with machine learning models have driven research into machine unlearning, which aims to erase the memory of specific target training data from already…
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…
DCMT: A Direct Entire-Space Causal Multi-Task Framework for Post-Click Conversion Estimation
Feng Zhu, Mingjie Zhong, Xinxing Yang +8
In recommendation scenarios, there are two long-standing challenges, i.e., selection bias and data sparsity, which lead to a significant drop in prediction accuracy for both Click-…
INCREASE: Inductive Graph Representation Learning for Spatio-Temporal Kriging
Chuanpan Zheng, Xiaoliang Fan, Cheng Wang +3
Spatio-temporal kriging is an important problem in web and social applications, such as Web or Internet of Things, where things (e.g., sensors) connected into a web often come with…
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…
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…
Adaptive Client Sampling in Federated Learning via Online Learning with Bandit Feedback
Boxin Zhao, Lingxiao Wang, Ziqi Liu +4
Due to the high cost of communication, federated learning (FL) systems need to sample a subset of clients that are involved in each round of training. As a result, client sampling…
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…
Survey and Open Problems in Privacy Preserving Knowledge Graph: Merging, Query, Representation, Completion and Applications
Chaochao Chen, Jamie Cui, Guanfeng Liu +2
Knowledge Graph (KG) has attracted more and more companies' attention for its ability to connect different types of data in meaningful ways and support rich data services. However,…
A Deep Framework for Cross-Domain and Cross-System Recommendations
Feng Zhu, Yan Wang, Chaochao Chen +3
Cross-Domain Recommendation (CDR) and Cross-System Recommendations (CSR) are two of the promising solutions to address the long-standing data sparsity problem in recommender system…
When Homomorphic Encryption Marries Secret Sharing: Secure Large-Scale Sparse Logistic Regression and Applications in Risk Control
Chaochao Chen, Jun Zhou, Li Wang +7
Logistic Regression (LR) is the most widely used machine learning model in industry for its efficiency, robustness, and interpretability. Due to the problem of data isolation and t…
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…
Secret Sharing based Secure Regressions with Applications
Chaochao Chen, Liang Li, Wenjing Fang +6
Nowadays, the utilization of the ever expanding amount of data has made a huge impact on web technologies while also causing various types of security concerns. On one hand, potent…
Adapted tree boosting for Transfer Learning
Wenjing Fang, Chaochao Chen, Bowen Song +3
Secure online transaction is an essential task for e-commerce platforms. Alipay, one of the world's leading cashless payment platform, provides the payment service to both merchant…
Privacy Preserving Point-of-interest Recommendation Using Decentralized Matrix Factorization
Chaochao Chen, Ziqi Liu, Peilin Zhao +2
Points of interest (POI) recommendation has been drawn much attention recently due to the increasing popularity of location-based networks, e.g., Foursquare and Yelp. Among the exi…
Industrial Scale Privacy Preserving Deep Neural Network
Longfei Zheng, Chaochao Chen, Yingting Liu +6
Deep Neural Network (DNN) has been showing great potential in kinds of real-world applications such as fraud detection and distress prediction. Meanwhile, data isolation has become…
Privacy Preserving PCA for Multiparty Modeling
Yingting Liu, Chaochao Chen, Longfei Zheng +4
In this paper, we present a general multiparty modeling paradigm with Privacy Preserving Principal Component Analysis (PPPCA) for horizontally partitioned data. PPPCA can accomplis…
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…
Secure Social Recommendation based on Secret Sharing
Chaochao Chen, Liang Li, Bingzhe Wu +3
Nowadays, privacy preserving machine learning has been drawing much attention in both industry and academy. Meanwhile, recommender systems have been extensively adopted by many com…
Heterogeneous Graph Neural Networks for Malicious Account Detection
Ziqi Liu, Chaochao Chen, Xinxing Yang +3
We present, GEM, the first heterogeneous graph neural network approach for detecting malicious accounts at Alipay, one of the world's leading mobile cashless payment platform. Our…