Social information filtering: algorithms for automating “word of mouth”
CHI '95 Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Privacy in e-commerce: examining user scenarios and privacy preferences
Proceedings of the 1st ACM conference on Electronic commerce
Analysis of recommendation algorithms for e-commerce
Proceedings of the 2nd ACM conference on Electronic commerce
On the design and quantification of privacy preserving data mining algorithms
PODS '01 Proceedings of the twentieth ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
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
Amazon.com Recommendations: Item-to-Item Collaborative Filtering
IEEE Internet Computing
Collaborative Filtering with Privacy
SP '02 Proceedings of the 2002 IEEE Symposium on Security and Privacy
On the Privacy Preserving Properties of Random Data Perturbation Techniques
ICDM '03 Proceedings of the Third IEEE International Conference on Data Mining
Evaluating collaborative filtering recommender systems
ACM Transactions on Information Systems (TOIS)
'I didn't buy it for myself' privacy and ecommerce personalization
Proceedings of the 2003 ACM workshop on Privacy in the electronic society
Deriving private information from randomized data
Proceedings of the 2005 ACM SIGMOD international conference on Management of data
IEEE Transactions on Knowledge and Data Engineering
Scalable collaborative filtering using cluster-based smoothing
Proceedings of the 28th annual international ACM SIGIR conference on Research and development in information retrieval
Privacy practices of Internet users: self-reports versus observed behavior
International Journal of Human-Computer Studies - Special isssue: HCI research in privacy and security is critical now
Collaborative Filtering by Mining Association Rules from User Access Sequences
WIRI '05 Proceedings of the International Workshop on Challenges in Web Information Retrieval and Integration
A Collaborative Filtering Algorithm Employing Genetic Clustering to Ameliorate the Scalability Issue
ICEBE '06 Proceedings of the IEEE International Conference on e-Business Engineering
Private distributed collaborative filtering using estimated concordance measures
Proceedings of the 2007 ACM conference on Recommender systems
Collaborative recommender systems: Combining effectiveness and efficiency
Expert Systems with Applications: An International Journal
Applications of wavelet data reduction in a recommender system
Expert Systems with Applications: An International Journal
Fuzzy-genetic approach to recommender systems based on a novel hybrid user model
Expert Systems with Applications: An International Journal
Alambic: a privacy-preserving recommender system for electronic commerce
International Journal of Information Security
A collaborative filtering method based on artificial immune network
Expert Systems with Applications: An International Journal
Optimal recursive clustering of likelihood functions for multiple object tracking
Pattern Recognition Letters
Collaborative filtering using orthogonal nonnegative matrix tri-factorization
Information Processing and Management: an International Journal
Collaborative filtering with the simple Bayesian classifier
PRICAI'00 Proceedings of the 6th Pacific Rim international conference on Artificial intelligence
Improving Privacy-Preserving NBC-Based Recommendations by Preprocessing
WI-IAT '10 Proceedings of the 2010 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology - Volume 01
A Hybrid Collaborative Filtering Algorithm Based on User-Item
ICCIS '10 Proceedings of the 2010 International Conference on Computational and Information Sciences
An improved privacy-preserving DWT-based collaborative filtering scheme
Expert Systems with Applications: An International Journal
An Improved Profile-Based CF Scheme with Privacy
ICSC '11 Proceedings of the 2011 IEEE Fifth International Conference on Semantic Computing
Empirical analysis of predictive algorithms for collaborative filtering
UAI'98 Proceedings of the Fourteenth conference on Uncertainty in artificial intelligence
Achieving private recommendations using randomized response techniques
PAKDD'06 Proceedings of the 10th Pacific-Asia conference on Advances in Knowledge Discovery and Data Mining
The impact of data obfuscation on the accuracy of collaborative filtering
Expert Systems with Applications: An International Journal
Evaluating collaborative filtering recommendations inside large learning object repositories
Information Processing and Management: an International Journal
A comparison of clustering-based privacy-preserving collaborative filtering schemes
Applied Soft Computing
Leveraging clustering approaches to solve the gray-sheep users problem in recommender systems
Expert Systems with Applications: An International Journal
An entropy-based neighbor selection approach for collaborative filtering
Knowledge-Based Systems
Robustness analysis of privacy-preserving model-based recommendation schemes
Expert Systems with Applications: An International Journal
| Hi-index | 0.00 |
Privacy-preserving collaborative filtering is an emerging web-adaptation tool to cope with information overload problem without jeopardizing individuals' privacy. However, collaborative filtering with privacy schemes commonly suffer from scalability and sparseness as the content in the domain proliferates. Moreover, applying privacy measures causes a distortion in collected data, which in turn defects accuracy of such systems. In this work, we propose a novel privacy-preserving collaborative filtering scheme based on bisecting k-means clustering in which we apply two preprocessing methods. The first preprocessing scheme deals with scalability problem by constructing a binary decision tree through a bisecting k-means clustering approach while the second produces clones of users by inserting pseudo-self-predictions into original user profiles to boost accuracy of scalability-enhanced structure. Sparse nature of collections are handled by transforming ratings into item features-based profiles. After analyzing our scheme with respect to privacy and supplementary costs, we perform experiments on benchmark data sets to evaluate it in terms of accuracy and online performance. Our empirical outcomes verify that combined effects of the proposed preprocessing schemes relieve scalability and augment accuracy significantly.