.png){kind=icon}
返回
Privacy-preserving collaborative filtering
.png){kind=icon}
PRE
AI
DOI：10.1080/10864415.2003.11044341.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Collaborative filtering (CF) techniques are becoming very popular on the Internet and are widely used in several domains to cope with information overload. E-commerce sites use filtering systems to recommend products to customers based on the preferences of like-minded customers, but their systems do not protect user privacy. Because users concerned about privacy may give false information, it is not easy to collect high-quality user data for collaborative filtering, and recommendation systems using poor data produce inaccurate recommendations. This means that privacy measures are key to the success of collecting high-quality data and providing accurate recommendations. This article discusses collaborative filtering with privacy based on both correlation and singular-value decomposition (SVD) and proposes the use of randomized perturbation techniques to protect user privacy while producing reasonably accurate recommendations. Such techniques add randomness to the original data, preventing the data collector (the server) from learning private user data, but this scheme can still provide accurate recommendations. Experiments were conducted with real datasets to evaluate the overall performance of the proposed scheme. The results were used for analysis of how different parameters affect accuracy. Collaborative filtering systems using randomized perturbation techniques were found to provide accurate recommendations while preserving user privacy.
Keyword:
accuracy
collaborative filtering
privacy
randomized perturbation
SVD
