Private distributed collaborative filtering using estimated concordance measures
Private distributed collaborative filtering using estimated concordance measures
复制标题
使用估计一致性度量的私有分布式协同过滤
DOI：
10.1145/1297231.1297233
复制
发表时间：
2007
期刊：
影响因子：
--
通讯作者：
Lathia N
中科院分区：
文献类型：
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作者：
Lathia N
协同过滤已经成为衡量用户相似度和预测用户兴趣的一种既定方法。然而，预测的准确性是以牺牲用户隐私为代价的：为了获得准确的相似性度量，用户需要相互分享他们的评级历史。在这项工作中，我们提出了一种新的相似性度量，它达到了与皮尔逊相关系数相当的预测精度，并且可以在不破坏用户隐私的情况下成功地进行估计。这种新颖的方法通过估计两个用户之间关于共享的随机评级集的一致、不一致和平分的评级对的数量来工作。在这样做时，既不会披露被评级的项目，也不会披露评级本身，从而实现严格保密的协作过滤。这项技术已经使用最近发布的Netflix大奖数据集进行了评估。
Collaborative filtering has become an established method to measure users' similarity and to make predictions about their interests. However, prediction accuracy comes at the cost of user's privacy: in order to derive accurate similarity measures, users are required to share their rating history with each other. In this work we propose a new measure of similarity, which achieves comparable prediction accuracy to the Pearson correlation coefficient, and that can successfully be estimated without breaking users' privacy. This novel method works by estimating the number of concordant, discordant and tied pairs of ratings between two users with respect to a shared random set of ratings. In doing so, neither the items rated nor the ratings themselves are disclosed, thus achieving strictly-private collaborative filtering. The technique has been evaluated using the recently released Netflix prize dataset.