A genre trust model for defending shilling attacks in recommender systems
Abstract Shilling attacks have been a significant vulnerability of collaborative filtering (CF) recommender systems, and trust in CF recommender algorithms has been proven to be helpful for improving the accuracy of system recommendations. As a few studies have been devoted to trust in this area, we...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
Springer
2021-04-01
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| Series: | Complex & Intelligent Systems |
| Subjects: | |
| Online Access: | https://doi.org/10.1007/s40747-021-00357-2 |