Chaochao Chen

Distributed Deep Forest and its Application to Automatic Detection of Cash-out Fraud

2018 · arXiv (Cornell University)

Internet companies are facing the need for handling large-scale machine learning applications on a daily basis and distributed implementation of machine learning algorithms which can handle extra-large scale tasks with great performance is widely needed. …

Privacy Preserving PCA for Multiparty Modeling

2020 · arXiv (Cornell University)

In this paper, we present a general multiparty modeling paradigm with Privacy Preserving Principal Component Analysis (PPPCA) for horizontally partitioned data. PPPCA can accomplish multiparty cooperative execution of PCA under the premise of keeping plaintext …

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

2020 · arXiv (Cornell University)

Deep Neural Networks (DNNs) have achieved remarkable progress in various real-world applications, especially when abundant training data are provided. However, data isolation has become a serious problem currently. Existing works build privacy preserving DNN models …

SimCDR: Preserving Intra-Domain Similarities of Users for Cross-Domain Recommendation

2025 · ACM Transactions on Information Systems

Cross-Domain Recommendation (CDR) can effectively alleviate the data sparsity issue in the recommendation system by transferring the source domain knowledge to the target domain. Many CDR methods try to find a mapping of latent embeddings …

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

2018 · Proceedings of the AAAI Conference on Artificial Intelligence

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 existing approaches to POI recommendation, Matrix Factorization (MF) based techniques …

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

2021 · IEEE Transactions on Knowledge and Data Engineering

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 of a source one with relatively richer information. However, most existing CDR …

DTCDR

2019

In order to address the data sparsity problem in recommender systems, in recent years, Cross-Domain Recommendation (CDR) leverages the relatively richer information from a source domain to improve the recommendation performance on a target domain …

A Graphical and Attentional Framework for Dual-Target Cross-Domain Recommendation

2020

The conventional single-target Cross-Domain Recommendation (CDR) only improves the recommendation accuracy on a target domain with the help of a source domain (with relatively richer information). In contrast, the novel dual-target CDR has been proposed …

Cross-Domain Recommendation: Challenges, Progress, and Prospects

2021

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 …

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

2018

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 systems. They leverage the relatively richer information, e.g., ratings, from the source domain …