کد مقاله کد نشریه سال انتشار مقاله انگلیسی نسخه تمام متن
495941 862845 2013 12 صفحه PDF دانلود رایگان
عنوان انگلیسی مقاله ISI
A comparison of clustering-based privacy-preserving collaborative filtering schemes
موضوعات مرتبط
مهندسی و علوم پایه مهندسی کامپیوتر نرم افزارهای علوم کامپیوتر
پیش نمایش صفحه اول مقاله
A comparison of clustering-based privacy-preserving collaborative filtering schemes
چکیده انگلیسی

Privacy-preserving collaborative filtering (PPCF) methods designate extremely beneficial filtering skills without deeply jeopardizing privacy. However, they mostly suffer from scalability, sparsity, and accuracy problems. First, applying privacy measures introduces additional costs making scalability worse. Second, due to randomness for preserving privacy, quality of predictions diminishes. Third, with increasing number of products, sparsity becomes an issue for both CF and PPCF schemes.In this study, we first propose a content-based profiling (CBP) of users to overcome sparsity issues while performing clustering because the very sparse nature of rating profiles sometimes do not allow strong discrimination. To cope with scalability and accuracy problems of PPCF schemes, we then show how to apply k-means clustering (KMC), fuzzy c-means method (FCM), and self-organizing map (SOM) clustering to CF schemes while preserving users’ confidentiality. After presenting an evaluation of clustering-based methods in terms of privacy and supplementary costs, we carry out real data-based experiments to compare the clustering algorithms within and against traditional CF and PPCF approaches in terms of accuracy. Our empirical outcomes demonstrate that FCM achieves the best low cost performance compared to other methods due to its approximation-based model. The results also show that our privacy-preserving methods are able to offer precise predictions.

Figure optionsDownload as PowerPoint slideHighlights
► Propose a novel content-based profiling method to alleviate sparsity-related problems.
► Utilize privacy-preserving measures on sparsity-enhanced profiles for providing predictions with privacy.
► Demonstrate the applicability of both conventional and soft non-hierarchical clustering techniques to the PPCF framework.
► Present a comparison among utilized clustering techniques in terms of privacy and accuracy.

ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Applied Soft Computing - Volume 13, Issue 5, May 2013, Pages 2478–2489
نویسندگان
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