Arbitrarily Distributed Data-Based Recommendations With Privacy
| Autor: | Huseyin Polat, Ibrahim Yakut |
|---|---|
| Přispěvatelé: | Anadolu Üniversitesi, Mühendislik Fakültesi, Bilgisayar Mühendisliği Bölümü |
| Rok vydání: | 2012 |
| Předmět: |
Scheme (programming language)
Information Systems and Management Privacy by Design Computer science Privacy software business.industry media_common.quotation_subject Internet privacy Computer security computer.software_genre Task (project management) Trustworthiness Arbitrarily Distributed Data Privacy Collaborative Filtering Collaborative filtering Data Mining Confidentiality Quality (business) business computer Accuracy media_common computer.programming_language |
| ISSN: | 0003-0007 |
| Popis: | WOS: 000300072600011 Collaborative filtering (CF) systems use customers' preferences about various products to offer recommendations. Providing accurate and reliable predictions is vital for both e-commerce companies and their customers. To offer such referrals, CF systems should have sufficient data. When data collected for CF purposes held by a central server, it is an easy task to provide recommendations. However, customers' preferences represented as ratings might be partitioned between two vendors. To supply trustworthy and correct predictions, such companies might desire to collaborate. Due to privacy concerns, financial fears, and legal issues; however, the parties may not want to disclose their data to each other. In this study, we scrutinize how to estimate item-based predictions on arbitrarily distributed data (ADD) between two e-commerce sites without deeply jeopardizing their privacy. We analyze our proposed scheme in terms of privacy; and demonstrate that the method does not intensely violate data owners' confidentiality. We conduct experiments using real data sets to show how coverage and quality of the predictions improve due to collaboration. We also investigate our scheme in terms of online performance; and demonstrate that supplementary online costs caused by privacy measures are negligible. Moreover, we perform trials to show how privacy concerns affect accuracy. Our results show that accuracy and coverage improve due to collaboration; and the proposed scheme is still able to offer truthful predictions with privacy concerns TUBITAK [108E221] This work is supported by grant 108E221 from TUBITAK. |
| Databáze: | OpenAIRE |
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