Collaborative Filtering by Personality Diagnosis: A Hybrid Memory and Model-Based Approach
UAI '00 Proceedings of the 16th Conference on Uncertainty in Artificial Intelligence
Privacy preserving association rule mining in vertically partitioned data
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
Privacy-preserving k-means clustering over vertically partitioned data
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
Privacy-Preserving Distributed Mining of Association Rules on Horizontally Partitioned Data
IEEE Transactions on Knowledge and Data Engineering
Privacy-Preserving Top-N Recommendation on Horizontally Partitioned Data
WI '05 Proceedings of the 2005 IEEE/WIC/ACM International Conference on Web Intelligence
Empirical analysis of predictive algorithms for collaborative filtering
UAI'98 Proceedings of the Fourteenth conference on Uncertainty in artificial intelligence
Privacy-preserving collaborative filtering on vertically partitioned data
PKDD'05 Proceedings of the 9th European conference on Principles and Practice of Knowledge Discovery in Databases
An improved privacy-preserving DWT-based collaborative filtering scheme
Expert Systems with Applications: An International Journal
Arbitrarily distributed data-based recommendations with privacy
Data & Knowledge Engineering
Privacy-preserving SOM-based recommendations on horizontally distributed data
Knowledge-Based Systems
Estimating NBC-based recommendations on arbitrarily partitioned data with privacy
Knowledge-Based Systems
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Data collected for collaborative filtering (CF) purposes might be split between various parties. Integrating such data is helpful for both e-companies and customers due to mutual advantageous. However, due to privacy reasons, data owners do not want to disclose their data. We hypothesize that if privacy measures are provided, data holders might decide to integrate their data to perform richer CF services. In this paper, we investigate how to achieve naïve Bayesian classifier (NBC)-based CF tasks on partitioned data with privacy. We perform experiments on real data, analyze our outcomes, and provide some suggestions.