GroupLens: an open architecture for collaborative filtering of netnews
CSCW '94 Proceedings of the 1994 ACM conference on Computer supported cooperative work
An algorithmic framework for performing collaborative filtering
Proceedings of the 22nd annual international ACM SIGIR conference on Research and development in information retrieval
Jester 2.0 (poster abstract): evaluation of an new linear time collaborative filtering algorithm
Proceedings of the 22nd annual international ACM SIGIR conference on Research and development in information retrieval
Item-based collaborative filtering recommendation algorithms
Proceedings of the 10th international conference on World Wide Web
Collaborative filtering with privacy via factor analysis
SIGIR '02 Proceedings of the 25th annual international ACM SIGIR conference on Research and development in information retrieval
Collaborative Filtering with Privacy
SP '02 Proceedings of the 2002 IEEE Symposium on Security and Privacy
'I didn't buy it for myself' privacy and ecommerce personalization
Proceedings of the 2003 ACM workshop on Privacy in the electronic society
Privacy-preserving eigentaste-based collaborative filtering
IWSEC'07 Proceedings of the Security 2nd international conference on Advances in information and computer security
Privacy-preserving SOM-based recommendations on horizontally distributed data
Knowledge-Based Systems
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Randomized perturbation techniques (RPT) are applied to perturb the customers' private data to protect privacy while providing accurate referrals. In the RPT-based collaborative filtering (CF) with privacy schemes, proposed so far, users disguise their ratings in the same way to achieve consistently perturbed data. However, since users might have different levels of concerns about their privacy, the customers might decide to perturb their private data differently, which causes inconsistently masked data. How, then, can e-companies present referrals using such data and how can inconsistent data disguising affect accuracy and privacy?