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
Neural Networks: A Comprehensive Foundation
Neural Networks: A Comprehensive Foundation
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
Eigentaste: A Constant Time Collaborative Filtering Algorithm
Information Retrieval
Collaborative Filtering with Privacy
SP '02 Proceedings of the 2002 IEEE Symposium on Security and Privacy
Evaluating collaborative filtering recommender systems
ACM Transactions on Information Systems (TOIS)
Privacy-preserving data integration and sharing
Proceedings of the 9th ACM SIGMOD workshop on Research issues in data mining and knowledge discovery
An Accurate and Scalable Collaborative Recommender
Artificial Intelligence Review
Privacy-Preserving Distributed Mining of Association Rules on Horizontally Partitioned Data
IEEE Transactions on Knowledge and Data Engineering
Privacy-preserving clustering with distributed EM mixture modeling
Knowledge and Information Systems
Scalable collaborative filtering using cluster-based smoothing
Proceedings of the 28th annual international ACM SIGIR conference on Research and development in information retrieval
IEEE Transactions on Knowledge and Data Engineering
PRIVATE-IYE: A Framework for Privacy Preserving Data Integration
ICDEW '06 Proceedings of the 22nd International Conference on Data Engineering Workshops
A privacy-preserving collaborative filtering scheme with two-way communication
EC '06 Proceedings of the 7th ACM conference on Electronic commerce
Effects of inconsistently masked data using RPT on CF with privacy
Proceedings of the 2007 ACM symposium on Applied computing
Data Clustering: Theory, Algorithms, and Applications (ASA-SIAM Series on Statistics and Applied Probability)
A new similarity measure for collaborative filtering to alleviate the new user cold-starting problem
Information Sciences: an International Journal
Private distributed collaborative filtering using estimated concordance measures
Proceedings of the 2007 ACM conference on Recommender systems
Privacy Preserving Collaborative Filtering Using Data Obfuscation
GRC '07 Proceedings of the 2007 IEEE International Conference on Granular Computing
Privacy-preserving top-N recommendation on distributed data
Journal of the American Society for Information Science and Technology
Providing Naïve Bayesian Classifier-Based Private Recommendations on Partitioned Data
PKDD 2007 Proceedings of the 11th European conference on Principles and Practice of Knowledge Discovery in Databases
Improving Prediction Quality in Collaborative Filtering Based on Clustering
WI-IAT '08 Proceedings of the 2008 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology - Volume 01
Improving memory-based collaborative filtering via similarity updating and prediction modulation
Information Sciences: an International Journal
Improved trust-aware recommender system using small-worldness of trust networks
Knowledge-Based Systems
Efficient privacy preserving distributed clustering based on secret sharing
PAKDD'07 Proceedings of the 2007 international conference on Emerging technologies in knowledge discovery and data mining
A new collaborative filtering metric that improves the behavior of recommender systems
Knowledge-Based Systems
Achieving private recommendations using randomized response techniques
PAKDD'06 Proceedings of the 10th Pacific-Asia conference on Advances in Knowledge Discovery and Data Mining
A collaborative filtering approach to mitigate the new user cold start problem
Knowledge-Based Systems
Incremental Collaborative Filtering recommender based on Regularized Matrix Factorization
Knowledge-Based Systems
A self-organizing network for hyperellipsoidal clustering (HEC)
IEEE Transactions on Neural Networks
Estimating NBC-based recommendations on arbitrarily partitioned data with privacy
Knowledge-Based Systems
Fast clustering-based anonymization approaches with time constraints for data streams
Knowledge-Based Systems
Knowledge-Based Systems
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To produce predictions with decent accuracy, collaborative filtering algorithms need sufficient data. Due to the nature of online shopping and increasing amount of online vendors, different customers' preferences about the same products can be distributed among various companies, even competing vendors. Therefore, those companies holding inadequate number of users' data might decide to combine their data in such a way to present accurate predictions with acceptable online performance. However, they do not want to divulge their data, because such data are considered confidential and valuable. Furthermore, it is not legal disclosing users' preferences; nevertheless, if privacy is protected, they can collaborate to produce correct predictions. We propose a privacy-preserving scheme to provide recommendations on horizontally partitioned data among multiple parties. In order to improve online performance, the parties cluster their distributed data off-line without greatly jeopardizing their secrecy. They then estimate predictions using k-nearest neighbor approach while preserving their privacy. We demonstrate that the proposed method preserves data owners' privacy and is able to suggest predictions resourcefully. By performing several experiments using real data sets, we analyze our scheme in terms of accuracy. Our empirical outcomes show that it is still possible to estimate truthful predictions competently while maintaining data owners' confidentiality based on horizontally distributed data.