Privacy-Preserving Collaborative Recommender Systems

Zhan, Justin; Hsieh, Chia-Lung; Wang, I-Cheng; Hsu, Tsan-sheng; Liau, Churn‐Jung; Wang, Dawei

doi:10.1109/tsmcc.2010.2040275

Cited by 100 publications

(41 citation statements)

References 22 publications

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“…Zhan et al [36] propose a privacy preserving approach in recommender system. As recommender system is successfully used in e-Commerce, users want relevant and precise recommendation and this can be possible only when two or more than two companies To avoid these hazards, cryptology approaches and scalar product protocol is used.…”

Section: B Issues In Recommender Systemsmentioning

confidence: 99%

A Survey on the Generation of Recommender Systems

Singh¹,

chuchra²,

Rani³

2017

IJIEEB

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Abstract-In the era of Internet, web is a giant source of information. The constantly growing rate of information in the web makes people confused to decide which product is relevant to them. To find relevant product in today's era is very time consuming and tedious task. Everyday a lot of information is uploaded and retrieved from the web. The web is overloaded with information and it is very essential to cop up with this overloaded and overlooked information. Recommender systems are the solution which can help a user to get relevant information from the bulk of information. Recommender systems provide customized or personalized and non personalized recommendations to interested users. Recommender systems are in its evolution stage. Recommender systems have been evolved from first generation to third generation through second generation. First generation or Web 1.0 recommender systems deal with E-commerce, Second generation or web 2.0 recommender systems use social network and social contextual information for accurate and diverse recommendations, and Third generation recommender systems use location based information or internet of things for generating recommendations. In this paper, three generation of recommender systems and are discussed. Similarity measures and evaluation metrics are used in these generations are also discussed.

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Section: B Issues In Recommender Systemsmentioning

confidence: 99%

A Survey on the Generation of Recommender Systems

Singh¹,

chuchra²,

Rani³

2017

IJIEEB

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“…A number of privacy preserving collaborative filtering (PPCF) algorithms have been proposed in RS literature (e.g. [15]) to protect user profiles from leakage. The major challenge in such systems is to preserve recommendation accuracy.…”

Section: Towards Ethical Recommender Systemsmentioning

confidence: 99%

Recommender Systems from an Industrial and Ethical Perspective

Paraschakis

2016

Proceedings of the 10th ACM Conference on Recommender Systems

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Over the recent years, a plethora of recommender systems (RS) have been proposed by academics. The degree of adoptability of these algorithms by industrial e-commerce platforms remains unclear. To get an insight into real-world recommendation engines, we survey more than 30 existing shopping cart solutions and compare the performance of popular recommendation algorithms on proprietary e-commerce datasets. Our results show that deployed systems rarely go beyond trivial "best seller" lists or very basic personalized recommendation algorithms, which nevertheless exhibit superior performance to more elaborate techniques both in our experiments and other related studies. We also perform chronological dataset splits to demonstrate the importance of preserving the sequence of events during evaluation, and the recency of events during training. The second part of our research is still ongoing and focuses on various ethical challenges that complicate the design of recommender systems. We believe that this direction of research remains mostly neglected despite its increasing impact on RS' quality and safety.

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“…In recommender systems [6], users want to improve useful endorsements without assigning their privacy. To accomplish so, a variety of privacy-preserving obliging filtering methods have been suggested by using techniques such as randomization, cryptography and so on.…”

Section: Privacy In Recommender Systemsmentioning

confidence: 99%

A Framework of Privacy Preserving QOS Prediction used for Web Service Recommendation

2015

IJSR

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Abstract:With increasing existence and reception of Web services on the World Wide Web, Quality-of-Service (QoS) is becoming essential for unfolding nonfunctional characteristics of Web services. Collaborative filtering (CF) techniques are becoming progressively popular with the progress of the Internet. To demeanor collaborative filtering, data from customers are needed. Though, gathering high quality data from customers is a difficult task because many customers are so concerned about their privacy that they might choose to give fabricated information. We propose a randomized disconcertion technique to safeguard user's privacy while still constructing accurate recommendations. Then, based on the collected QoS data, a collaborative filtering approach is designed to calculate Web service QoS.

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Privacy-Preserving Collaborative Recommender Systems

Cited by 100 publications

References 22 publications

A Survey on the Generation of Recommender Systems

A Survey on the Generation of Recommender Systems

Recommender Systems from an Industrial and Ethical Perspective

A Framework of Privacy Preserving QOS Prediction used for Web Service Recommendation

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