Social information filtering: algorithms for automating “word of mouth”
CHI '95 Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Item-based collaborative filtering recommendation algorithms
Proceedings of the 10th international conference on World Wide Web
Latent semantic models for collaborative filtering
ACM Transactions on Information Systems (TOIS)
Scalable collaborative filtering using cluster-based smoothing
Proceedings of the 28th annual international ACM SIGIR conference on Research and development in information retrieval
DCFLA: A distributed collaborative-filtering neighbor-locating algorithm
Information Sciences: an International Journal
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Proceedings of the 2007 ACM conference on Recommender systems
Distributed collaborative filtering with domain specialization
Proceedings of the 2007 ACM conference on Recommender systems
Differentially private recommender systems: building privacy into the net
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
Preserving privacy in collaborative filtering through distributed aggregation of offline profiles
Proceedings of the third ACM conference on Recommender systems
Differential privacy: a survey of results
TAMC'08 Proceedings of the 5th international conference on Theory and applications of models of computation
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
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Traditional collaborative filtering (CF) methods suffer from sparse or even cold-start problems, especially for new established recommenders. However, since there are now quite a few recommender systems already existing in good working order, their data should be valuable to the new-start recommenders. This paper proposes shared collaborative filtering approach to leverage the data from other parties (contributor party) to improve own (beneficiary party's) CF performance, and at the same time the privacy of other parties cannot be compromised. Item neighborhood list is chosen as the shared data from the contributor party with considering differential privacy. And an innovative algorithm called neighborhood boosting is proposed to make the beneficiary party leverage the shared data. MovieLens and Netflix data sets are considered as two parties to simulate and evaluate the proposed shared CF approach. The experiment results validate the positive effects of shared CF for increasing the recommendation accuracy of the beneficiary party. Especially when the beneficiary party's data is quite sparse, the performance can be increased by around 10%. The experiments also show that shared CF even outperforms the methods that incorporate the detailed original rating scores of the contributor party without considering the privacy issues. The proposed shared CF approach obtains a win-win situation for both performance and privacy.