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Handling the Data Growth with Privacy Preservation in Collaborative Filtering

机译：在协作过滤中使用隐私保护处理数据增长

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摘要

The emergence of electric business facilitates people in purchasing merchandises over the Internet. To sell the products better, online service providers use recommender systems to provide recommendations to customers. Most recom-mender systems are based on collaborative filtering (CF) technique. This technique provides recommendations based on users' transaction history. Due to the technical limitations, many online merchants ask a third party to help develop and maintain recommender systems instead of doing that themselves. Therefore, they need to share their data with these third parties and users' private information is prone to leaking. Furthermore, the fast data growth should be handled by the data owner efficiently without sacrificing privacy. In this chapter, we propose a privacy preserving data updating scheme for collaborative filtering purpose and study its performance on two different datasets. The experimental results show that the proposed scheme does not degrade recommendation accuracy and can preserve a satisfactory level of privacy while updating the data efficiently.
机译：电子商务的出现促进了人们通过Internet购买商品。为了更好地销售产品，在线服务提供商使用推荐系统向客户提供推荐。大多数推荐系统基于协作过滤（CF）技术。该技术基于用户的交易历史记录提供建议。由于技术限制，许多在线商家要求第三方来帮助开发和维护推荐系统，而不是自己这样做。因此，他们需要与这些第三方共享其数据，并且用户的私人信息容易泄漏。此外，数据所有者应在不牺牲隐私的情况下有效地处理快速增长的数据。在本章中，我们提出用于协作过滤目的的隐私保护数据更新方案，并研究其在两个不同数据集上的性能。实验结果表明，该方案不会降低推荐的准确性，并且可以在有效更新数据的同时保持令人满意的隐私级别。

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