From existing trends to future trends in privacy‐preserving collaborative filtering. (28th August 2015)
- Record Type:
- Journal Article
- Title:
- From existing trends to future trends in privacy‐preserving collaborative filtering. (28th August 2015)
- Main Title:
- From existing trends to future trends in privacy‐preserving collaborative filtering
- Authors:
- Ozturk, Adem
Polat, Huseyin - Abstract:
- <abstract abstract-type="main" id="widm1163-abs-0001"> <title> <x xml:space="preserve">Abstract</x> </title> <p id="widm1163-para-0001">The <italic>information overload problem</italic>, also known as <italic>infobesity</italic>, forces online vendors to utilize collaborative filtering algorithms. Although various recommendation methods are widely used by many electronic commerce sites, they still have substantial problems, including but not limited to privacy, accuracy, online performance, scalability, cold start, coverage, grey sheep, robustness, being subject to shilling attacks, diversity, data sparsity, and synonymy. Privacy‐preserving collaborative filtering methods have been proposed to handle the privacy problem. Due to the increasing popularity of privacy protection and recommendation estimation over the Internet, prediction schemes with privacy are still receiving increasing attention. Because research trends might change over time, it is critical for researchers to observe future trends. In this study, we determine the existing trends in the privacy‐preserving collaborative filtering field by examining the related papers published mainly in the last few years. Comprehensive examinations of the most up‐to‐date related studies are described. By scrutinizing the contemporary inclinations, we present the most promising possible research trends in the near future. Our proposals can help interested researchers direct their research toward better outcomes and might open<abstract abstract-type="main" id="widm1163-abs-0001"> <title> <x xml:space="preserve">Abstract</x> </title> <p id="widm1163-para-0001">The <italic>information overload problem</italic>, also known as <italic>infobesity</italic>, forces online vendors to utilize collaborative filtering algorithms. Although various recommendation methods are widely used by many electronic commerce sites, they still have substantial problems, including but not limited to privacy, accuracy, online performance, scalability, cold start, coverage, grey sheep, robustness, being subject to shilling attacks, diversity, data sparsity, and synonymy. Privacy‐preserving collaborative filtering methods have been proposed to handle the privacy problem. Due to the increasing popularity of privacy protection and recommendation estimation over the Internet, prediction schemes with privacy are still receiving increasing attention. Because research trends might change over time, it is critical for researchers to observe future trends. In this study, we determine the existing trends in the privacy‐preserving collaborative filtering field by examining the related papers published mainly in the last few years. Comprehensive examinations of the most up‐to‐date related studies are described. By scrutinizing the contemporary inclinations, we present the most promising possible research trends in the near future. Our proposals can help interested researchers direct their research toward better outcomes and might open new ways to enrich privacy‐preserving collaborative filtering studies. <italic>WIREs Data Mining Knowl Discov</italic> 2015, 5:276–291. doi: 10.1002/widm.1163</p> <p>For further resources related to this article, please visit the <ext-link ext-link-type="uri" xlink:href="http://wires.wiley.com/remdoi.cgi?doi=10.1002/widm.1163" xlink:type="simple" xmlns:xlink="http://www.w3.org/1999/xlink">WIREs website</ext-link>.</p> </abstract> … (more)
- Is Part Of:
- Wiley interdisciplinary reviews. Volume 5:Number 6(2015)
- Journal:
- Wiley interdisciplinary reviews
- Issue:
- Volume 5:Number 6(2015)
- Issue Display:
- Volume 5, Issue 6 (2015)
- Year:
- 2015
- Volume:
- 5
- Issue:
- 6
- Issue Sort Value:
- 2015-0005-0006-0000
- Page Start:
- 276
- Page End:
- 291
- Publication Date:
- 2015-08-28
- Subjects:
- Data mining -- Periodicals
006.31205 - Journal URLs:
- http://onlinelibrary.wiley.com/journal/10.1002/(ISSN)1942-4795 ↗
http://onlinelibrary.wiley.com/ ↗ - DOI:
- 10.1002/widm.1163 ↗
- Languages:
- English
- ISSNs:
- 1942-4787
- Deposit Type:
- Legaldeposit
- View Content:
- Available online (eLD content is only available in our Reading Rooms) ↗
- Physical Locations:
- British Library DSC - BLDSS-3PM
British Library HMNTS - ELD Digital store - Ingest File:
- 4258.xml