Item-based collaborative filtering recommendation algorithms
Proceedings of the 10th international conference on World Wide Web
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
Collaborative Filtering with Privacy
SP '02 Proceedings of the 2002 IEEE Symposium on Security and Privacy
Privacy-Preserving Collaborative Filtering Using Randomized Perturbation Techniques
ICDM '03 Proceedings of the Third IEEE International Conference on Data Mining
SVD-based collaborative filtering with privacy
Proceedings of the 2005 ACM symposium on Applied computing
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
Using Singular Value Decomposition Approximation for Collaborative Filtering
CEC '05 Proceedings of the Seventh IEEE International Conference on E-Commerce Technology
A Collaborative Filtering Algorithm Employing Genetic Clustering to Ameliorate the Scalability Issue
ICEBE '06 Proceedings of the IEEE International Conference on e-Business Engineering
A novel algorithm for wavelet based ECG signal coding
Computers and Electrical Engineering
Using SVD and demographic data for the enhancement of generalized Collaborative Filtering
Information Sciences: an International Journal
Effective missing data prediction for collaborative filtering
SIGIR '07 Proceedings of the 30th annual international ACM SIGIR conference on Research and development in information retrieval
An Architecture for Privacy Preserving Collaborative Filtering on Web Portals
IAS '07 Proceedings of the Third International Symposium on Information Assurance and Security
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
Discrete wavelet transform-based multivariate exploration of tissue via imaging mass spectrometry
Proceedings of the 2008 ACM symposium on Applied computing
Fuzzy-genetic approach to recommender systems based on a novel hybrid user model
Expert Systems with Applications: An International Journal
Using error-correcting dependencies for collaborative filtering
Data & Knowledge Engineering
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
A random walk method for alleviating the sparsity problem in collaborative filtering
Proceedings of the 2008 ACM conference on Recommender systems
A collaborative filtering method based on artificial immune network
Expert Systems with Applications: An International Journal
Collaborative filtering using orthogonal nonnegative matrix tri-factorization
Information Processing and Management: an International Journal
Selecting a small number of products for effective user profiling in collaborative filtering
Expert Systems with Applications: An International Journal
Improving memory-based collaborative filtering via similarity updating and prediction modulation
Information Sciences: an International Journal
On the Performance of SVD-Based Algorithms for Collaborative Filtering
BCI '09 Proceedings of the 2009 Fourth Balkan Conference in Informatics
Collaborative filtering based on iterative principal component analysis
Expert Systems with Applications: An International Journal
A cluster-based wavelet feature extraction method and its application
Engineering Applications of Artificial Intelligence
Privacy-preserving eigentaste-based collaborative filtering
IWSEC'07 Proceedings of the Security 2nd international conference on Advances in information and computer security
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 comparison of clustering-based privacy-preserving collaborative filtering schemes
Applied Soft Computing
Knowledge-Based Systems
A scalable privacy-preserving recommendation scheme via bisecting k-means clustering
Information Processing and Management: an International Journal
Robustness analysis of privacy-preserving model-based recommendation schemes
Expert Systems with Applications: An International Journal
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Collaborative filtering (CF) is one of the most efficient techniques to produce personalized recommendations and to deal with the information overload of modern times. Although CF techniques have immensely useful filtering capabilities, many CF systems have challenging problems like scalability, accuracy, and privacy. One approach to enhance scalability of such systems is to apply discrete wavelet transformation (DWT) techniques. DWT-based CF schemes significantly overcome the scalability problem. However, they fail to protect individual users' privacy. Moreover, although such schemes provide accurate predictions, the quality of the recommendations provided by DWT-based CF schemes can be further improved by applying some preprocessing methods. In this study, we propose privacy-preserving schemes to produce accurate predictions based on DWT efficiently without deeply exposing customers' privacy. We also recommend methods to order items before applying DWT to boost accuracy. After evaluating our schemes in terms of privacy and supplementary costs, we perform real data-based experiments to scrutinize the proposed schemes in terms of accuracy. Experimental results show that our privacy-preserving methods are able to offer recommendations with decent accuracy. Moreover, our outcomes show that our methods utilized to sort items improve accuracy. We finally provide some suggestions and explain future works.