In order to provide high-quality recommendations for users, it is desirable
to share and integrate multiple datasets held by different parties. However,
when sharing such distributed datasets, we need to protect personal and
confidential information contained in the datasets. To this end, we establish a
framework for privacy-preserving recommender systems using the data
collaboration analysis of distributed datasets. Numerical experiments with two
public rating datasets demonstrate that our privacy-preserving method for
rating prediction can improve the prediction accuracy for distributed datasets.
This study opens up new possibilities for privacy-preserving techniques in
recommender systems.

建立了一个隐私保护推荐系统的框架，通过对分布式数据集的数据协作分析，改进了分布式数据集的评级预测准确性。

使用数据协作分析的隐私保护推荐系统

Privacy-preserving recommender system using the data collaboration  analysis for distributed datasets

Given the time and expense associated with bringing a drug to market,
numerous studies have been conducted to predict the properties of compounds
based on their structure using machine learning. Federated learning has been
applied to compound datasets to increase their prediction accuracy while
safeguarding potentially proprietary information. However, federated learning
is encumbered by low accuracy in not identically and independently distributed
(non-IID) settings, i.e., data partitioning has a large label bias, and is
considered unsuitable for compound datasets, which tend to have large label
bias. To address this limitation, we utilized an alternative method of
distributed machine learning to chemical compound data from open sources,
called data collaboration analysis (DC). We also proposed data collaboration
analysis using projection data (DCPd), which is an improved method that
utilizes auxiliary PubChem data. This improves the quality of individual
user-side data transformations for the projection data for the creation of
intermediate representations. The classification accuracy, i.e., area under the
curve in the receiver operating characteristic curve (ROC-AUC) and AUC in the
precision-recall curve (PR-AUC), of federated averaging (FedAvg), DC, and DCPd
was compared for five compound datasets. We determined that the machine
learning performance for non-IID settings was in the order of DCPd, DC, and
FedAvg, although they were almost the same in identically and independently
distributed (IID) settings. Moreover, the results showed that compared to other
methods, DCPd exhibited a negligible decline in classification accuracy in
experiments with different degrees of label bias. Thus, DCPd can address the
low performance in non-IID settings, which is one of the challenges of
federated learning.

使用分布式机器学习方法，通过数据协同分析和投影数据，改进了联邦学习系统，提高了药物预测的准确性和分类性能。