Search Results for author: Chaochao Chen

Found 50 papers, 7 papers with code

PPGenCDR: A Stable and Robust Framework for Privacy-Preserving Cross-Domain Recommendation

no code implementations11 May 2023 Xinting Liao, Weiming Liu, Xiaolin Zheng, Binhui Yao, Chaochao Chen

Privacy-preserving cross-domain recommendation (PPCDR) refers to preserving the privacy of users when transferring the knowledge from source domain to target domain for better performance, which is vital for the long-term development of recommender systems.

Privacy Preserving Recommendation Systems

Selective and Collaborative Influence Function for Efficient Recommendation Unlearning

no code implementations20 Apr 2023 Yuyuan Li, Chaochao Chen, Xiaolin Zheng, Yizhao Zhang, Biao Gong, Jun Wang

In this paper, we first identify two main disadvantages of directly applying existing unlearning methods in the context of recommendation, i. e., (i) unsatisfactory efficiency for large-scale recommendation models and (ii) destruction of collaboration across users and items.

Recommendation Systems

DCMT: A Direct Entire-Space Causal Multi-Task Framework for Post-Click Conversion Estimation

no code implementations13 Feb 2023 Feng Zhu, Mingjie Zhong, Xinxing Yang, Longfei Li, Lu Yu, Tiehua Zhang, Jun Zhou, Chaochao Chen, Fei Wu, Guanfeng Liu, Yan Wang

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-Through Rate (CTR) and post-click Conversion Rate (CVR) tasks.

Multi-Task Learning Selection bias

INCREASE: Inductive Graph Representation Learning for Spatio-Temporal Kriging

1 code implementation6 Feb 2023 Chuanpan Zheng, Xiaoliang Fan, Cheng Wang, Jianzhong Qi, Chaochao Chen, Longbiao Chen

It aims to infer knowledge for (the things at) unobserved locations using the data from (the things at) observed locations during a given time period of interest.

Graph Representation Learning

Heterogeneous Information Crossing on Graphs for Session-based Recommender Systems

no code implementations24 Oct 2022 Xiaolin Zheng, Rui Wu, Zhongxuan Han, Chaochao Chen, Linxun Chen, Bing Han

HICG utilizes multiple types of user behaviors in the sessions to construct heterogeneous graphs, and captures users' current interests with their long-term preferences by effectively crossing the heterogeneous information on the graphs.

Contrastive Learning Recommendation Systems

Making Split Learning Resilient to Label Leakage by Potential Energy Loss

no code implementations18 Oct 2022 Fei Zheng, Chaochao Chen, Binhui Yao, Xiaolin Zheng

As a practical privacy-preserving learning method, split learning has drawn much attention in academia and industry.

Privacy Preserving

DDGHM: Dual Dynamic Graph with Hybrid Metric Training for Cross-Domain Sequential Recommendation

no code implementations21 Sep 2022 Xiaolin Zheng, Jiajie Su, Weiming Liu, Chaochao Chen

However, the short interaction sequences limit the performance of existing SR. To solve this problem, we focus on Cross-Domain Sequential Recommendation (CDSR) in this paper, which aims to leverage information from other domains to improve the sequential recommendation performance of a single domain.

Metric Learning Sequential Recommendation

Cross-Network Social User Embedding with Hybrid Differential Privacy Guarantees

1 code implementation4 Sep 2022 Jiaqian Ren, Lei Jiang, Hao Peng, Lingjuan Lyu, Zhiwei Liu, Chaochao Chen, Jia Wu, Xu Bai, Philip S. Yu

Integrating multiple online social networks (OSNs) has important implications for many downstream social mining tasks, such as user preference modelling, recommendation, and link prediction.

Link Prediction Network Embedding +1

Exploiting Variational Domain-Invariant User Embedding for Partially Overlapped Cross Domain Recommendation

no code implementations13 May 2022 Weiming Liu, Xiaolin Zheng, Mengling Hu, Chaochao Chen

Most of the existing CDR models assume that both the source and target domains share the same overlapped user set for knowledge transfer.

Recommendation Systems Transfer Learning +1

Making Recommender Systems Forget: Learning and Unlearning for Erasable Recommendation

no code implementations22 Mar 2022 Yuyuan Li, Xiaolin Zheng, Chaochao Chen, Junlin Liu

The basic idea of most recommender systems is collaborative filtering, but existing MU methods ignore the collaborative information across users and items.

Collaborative Filtering Recommendation Systems

Exploiting Data Sparsity in Secure Cross-Platform Social Recommendation

no code implementations NeurIPS 2021 Jamie Cui, Chaochao Chen, Lingjuan Lyu, Carl Yang, Li Wang

As a result, our model can not only improve the recommendation performance of the rating platform by incorporating the sparse social data on the social platform, but also protect data privacy of both platforms.

Information Retrieval Retrieval

Differential Private Knowledge Transfer for Privacy-Preserving Cross-Domain Recommendation

no code implementations10 Feb 2022 Chaochao Chen, Huiwen Wu, Jiajie Su, Lingjuan Lyu, Xiaolin Zheng, Li Wang

To this end, PriCDR can not only protect the data privacy of the source domain, but also alleviate the data sparsity of the source domain.

Privacy Preserving Recommendation Systems +1

Adaptive Client Sampling in Federated Learning via Online Learning with Bandit Feedback

1 code implementation28 Dec 2021 Boxin Zhao, Lingxiao Wang, Mladen Kolar, Ziqi Liu, Zhiqiang Zhang, Jun Zhou, Chaochao Chen

As a result, client sampling plays an important role in FL systems as it affects the convergence rate of optimization algorithms used to train machine learning models.

Federated Learning Stochastic Optimization

Leveraging Distribution Alignment via Stein Path for Cross-Domain Cold-Start Recommendation

no code implementations NeurIPS 2021 Weiming Liu, Jiajie Su, Chaochao Chen, Xiaolin Zheng

To address this issue, we propose DisAlign, a cross-domain recommendation framework for the CDCSR problem, which utilizes both rating and auxiliary representations from the source domain to improve the recommendation performance of the target domain.

Recommendation Systems

A Unified Framework for Cross-Domain and Cross-System Recommendations

no code implementations18 Aug 2021 Feng Zhu, Yan Wang, Jun Zhou, Chaochao Chen, Longfei Li, Guanfeng Liu

Moreover, to avoid negative transfer, we further propose a Personalized training strategy to minimize the embedding difference of common entities between a richer dataset and a sparser dataset, deriving three new models, i. e., GA-DTCDR-P, GA-MTCDR-P, and GA-CDR+CSR-P, for the three scenarios respectively.

Graph Embedding

Towards Secure and Practical Machine Learning via Secret Sharing and Random Permutation

1 code implementation17 Aug 2021 Fei Zheng, Chaochao Chen, Xiaolin Zheng, Mingjie Zhu

Since our method reduces the cost for element-wise function computation, it is more efficient than existing cryptographic methods.

BIG-bench Machine Learning Privacy Preserving +1

Cross-Domain Recommendation: Challenges, Progress, and Prospects

no code implementations2 Mar 2021 Feng Zhu, Yan Wang, Chaochao Chen, Jun Zhou, Longfei Li, Guanfeng Liu

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 from a richer domain to improve the recommendation performance in a sparser domain.

Recommendation Systems

Towards Scalable and Privacy-Preserving Deep Neural Network via Algorithmic-Cryptographic Co-design

no code implementations17 Dec 2020 Jun Zhou, Longfei Zheng, Chaochao Chen, Yan Wang, Xiaolin Zheng, Bingzhe Wu, Cen Chen, Li Wang, Jianwei Yin

In this paper, we propose SPNN - a Scalable and Privacy-preserving deep Neural Network learning framework, from algorithmic-cryptographic co-perspective.

Privacy Preserving

Survey and Open Problems in Privacy Preserving Knowledge Graph: Merging, Query, Representation, Completion and Applications

no code implementations20 Nov 2020 Chaochao Chen, Jamie Cui, Guanfeng Liu, Jia Wu, Li Wang

In this paper, to fill this gap, we summarize the open problems for privacy preserving KG in data isolation setting and propose possible solutions for them.

Privacy Preserving

ASFGNN: Automated Separated-Federated Graph Neural Network

no code implementations6 Nov 2020 Longfei Zheng, Jun Zhou, Chaochao Chen, Bingzhe Wu, Li Wang, Benyu Zhang

Specifically, to solve the data Non-IID problem, we first propose a separated-federated GNN learning model, which decouples the training of GNN into two parts: the message passing part that is done by clients separately, and the loss computing part that is learnt by clients federally.

Bayesian Optimization

A Deep Framework for Cross-Domain and Cross-System Recommendations

no code implementations14 Sep 2020 Feng Zhu, Yan Wang, Chaochao Chen, Guanfeng Liu, Mehmet Orgun, Jia Wu

Therefore, finding an accurate mapping of the latent factors across domains or systems is crucial to enhancing recommendation accuracy.

Recommendation Systems

When Homomorphic Encryption Marries Secret Sharing: Secure Large-Scale Sparse Logistic Regression and Applications in Risk Control

no code implementations20 Aug 2020 Chaochao Chen, Jun Zhou, Li Wang, Xibin Wu, Wenjing Fang, Jin Tan, Lei Wang, Alex X. Liu, Hao Wang, Cheng Hong

In this paper, we first present CAESAR, which combines HE and SS to build secure large-scale sparse logistic regression model and achieves both efficiency and security.


Vertically Federated Graph Neural Network for Privacy-Preserving Node Classification

no code implementations25 May 2020 Chaochao Chen, Jun Zhou, Longfei Zheng, Huiwen Wu, Lingjuan Lyu, Jia Wu, Bingzhe Wu, Ziqi Liu, Li Wang, Xiaolin Zheng

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 different nodes.

Classification General Classification +2

Large-Scale Secure XGB for Vertical Federated Learning

no code implementations18 May 2020 Wenjing Fang, Derun Zhao, Jin Tan, Chaochao Chen, Chaofan Yu, Li Wang, Lei Wang, Jun Zhou, Benyu Zhang

Privacy-preserving machine learning has drawn increasingly attention recently, especially with kinds of privacy regulations come into force.

BIG-bench Machine Learning Federated Learning +1

Secret Sharing based Secure Regressions with Applications

no code implementations10 Apr 2020 Chaochao Chen, Liang Li, Wenjing Fang, Jun Zhou, Li Wang, Lei Wang, Shuang Yang, Alex Liu, Hao Wang

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.


Privacy Preserving Point-of-interest Recommendation Using Decentralized Matrix Factorization

no code implementations12 Mar 2020 Chaochao Chen, Ziqi Liu, Peilin Zhao, Jun Zhou, Xiaolong Li

However, existing MF approaches suffer from two major problems: (1) Expensive computations and storages due to the centralized model training mechanism: the centralized learners have to maintain the whole user-item rating matrix, and potentially huge low rank matrices.

Privacy Preserving

Industrial Scale Privacy Preserving Deep Neural Network

no code implementations11 Mar 2020 Longfei Zheng, Chaochao Chen, Yingting Liu, Bingzhe Wu, Xibin Wu, Li Wang, Lei Wang, Jun Zhou, Shuang Yang

Deep Neural Network (DNN) has been showing great potential in kinds of real-world applications such as fraud detection and distress prediction.

Fraud Detection Privacy Preserving

Practical Privacy Preserving POI Recommendation

no code implementations5 Mar 2020 Chaochao Chen, Jun Zhou, Bingzhe Wu, Wenjin Fang, Li Wang, Yuan Qi, Xiaolin Zheng

Meanwhile, the public data need to be accessed by all the users are kept by the recommender to reduce the storage costs of users' devices.

Federated Learning Privacy Preserving

Heterogeneous Graph Neural Networks for Malicious Account Detection

1 code implementation27 Feb 2020 Ziqi Liu, Chaochao Chen, Xinxing Yang, Jun Zhou, Xiaolong Li, Le Song

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.

How Much Can A Retailer Sell? Sales Forecasting on Tmall

no code implementations27 Feb 2020 Chaochao Chen, Ziqi Liu, Jun Zhou, Xiaolong Li, Yuan Qi, Yujing Jiao, Xingyu Zhong

By analyzing the data, we have two main observations, i. e., sales seasonality after we group different groups of retails and a Tweedie distribution after we transform the sales (target to forecast).

regression Time Series Forecasting

Privacy Preserving PCA for Multiparty Modeling

no code implementations6 Feb 2020 Yingting Liu, Chaochao Chen, Longfei Zheng, Li Wang, Jun Zhou, Guiquan Liu, Shuang Yang

In this paper, we present a general multiparty modeling paradigm with Privacy Preserving Principal Component Analysis (PPPCA) for horizontally partitioned data.

Fraud Detection Privacy Preserving

Secure Social Recommendation based on Secret Sharing

no code implementations6 Feb 2020 Chaochao Chen, Liang Li, Bingzhe Wu, Cheng Hong, Li Wang, Jun Zhou

It is well known that social information, which is rich on social platforms such as Facebook, are useful to recommender systems.

Privacy Preserving Recommendation Systems

A Time Attention based Fraud Transaction Detection Framework

no code implementations26 Dec 2019 Longfei Li, Ziqi Liu, Chaochao Chen, Ya-Lin Zhang, Jun Zhou, Xiaolong Li

With online payment platforms being ubiquitous and important, fraud transaction detection has become the key for such platforms, to ensure user account safety and platform security.

Quantification of the Leakage in Federated Learning

no code implementations12 Oct 2019 Zhaorui Li, Zhicong Huang, Chaochao Chen, Cheng Hong

In this paper, we discuss the leakage based on a federated approximated logistic regression model and show that such gradient's leakage could leak the complete training data if all elements of the inputs are either 0 or 1.

Cryptography and Security

Characterizing Membership Privacy in Stochastic Gradient Langevin Dynamics

no code implementations5 Oct 2019 Bingzhe Wu, Chaochao Chen, Shiwan Zhao, Cen Chen, Yuan YAO, Guangyu Sun, Li Wang, Xiaolu Zhang, Jun Zhou

Based on this framework, we demonstrate that SGLD can prevent the information leakage of the training dataset to a certain extent.

Generalization Bounds

Generalization in Generative Adversarial Networks: A Novel Perspective from Privacy Protection

no code implementations NeurIPS 2019 Bingzhe Wu, Shiwan Zhao, Chaochao Chen, Haoyang Xu, Li Wang, Xiaolu Zhang, Guangyu Sun, Jun Zhou

In this paper, we aim to understand the generalization properties of generative adversarial networks (GANs) from a new perspective of privacy protection.

FinBrain: When Finance Meets AI 2.0

no code implementations26 Aug 2018 Xiaolin Zheng, Mengying Zhu, Qibing Li, Chaochao Chen, Yanchao Tan

Artificial intelligence (AI) is the core technology of technological revolution and industrial transformation.

Decision Making Management

Distributed Collaborative Hashing and Its Applications in Ant Financial

no code implementations13 Apr 2018 Chaochao Chen, Ziqi Liu, Peilin Zhao, Longfei Li, Jun Zhou, Xiaolong Li

The experimental results demonstrate that, comparing with the classic and state-of-the-art (distributed) latent factor models, DCH has comparable performance in terms of recommendation accuracy but has both fast convergence speed in offline model training procedure and realtime efficiency in online recommendation procedure.

Collaborative Filtering

Time-sensitive Customer Churn Prediction based on PU Learning

no code implementations27 Feb 2018 Li Wang, Chaochao Chen, Jun Zhou, Xiaolong Li

With the fast development of Internet companies throughout the world, customer churn has become a serious concern.

GeniePath: Graph Neural Networks with Adaptive Receptive Paths

3 code implementations3 Feb 2018 Ziqi Liu, Chaochao Chen, Longfei Li, Jun Zhou, Xiaolong Li, Le Song, Yuan Qi

We present, GeniePath, a scalable approach for learning adaptive receptive fields of neural networks defined on permutation invariant graph data.

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