Abstract
Collaborative Filtering (CF) is considered a powerful technique for generating personalized recommendations. Centralized storage of user profiles in CF systems presents a privacy breach, since the profiles are available to other users. Recent works proposed enhancing the privacy of the CF by distributing the profiles between multiple repositories. This work investigates how a
decentralized distributed storage of user profiles combined with data perturbation techniques mitigates the privacy issues. Experiments, conducted on three datasets, show that relatively large parts of the profiles can be perturbed without hampering the accuracy of the CF. The experiments also allow conclusion to be drawn regarding the specific users and parts of the profiles that are valuable for generating accurate CF predictions.
decentralized distributed storage of user profiles combined with data perturbation techniques mitigates the privacy issues. Experiments, conducted on three datasets, show that relatively large parts of the profiles can be perturbed without hampering the accuracy of the CF. The experiments also allow conclusion to be drawn regarding the specific users and parts of the profiles that are valuable for generating accurate CF predictions.
Original language | English |
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Title of host publication | Proceedings of the ECAI 2006 Workshop on Recommender Systems |
Pages | 49-53 |
Number of pages | 5 |
Publication status | Published - 2006 |
Externally published | Yes |
Event | 17th European Conference on Artificial Intelligence (ECAI 2006) - Riva del Garda, Italy Duration: 29 Aug 2006 → 1 Sept 2006 |
Conference
Conference | 17th European Conference on Artificial Intelligence (ECAI 2006) |
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Country/Territory | Italy |
City | Riva del Garda |
Period | 29/08/06 → 1/09/06 |