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- W2765866812 abstract "With the ever-increasing number of web services on the Web, various service recommendation techniques, e.g., User-based Collaborative Filtering (UCF) have been developed to alleviate the service selection burden of a target user. In traditional UCF-based recommendation approaches, the first key step is to find the candidate users who have the largest invoked-service intersection with the target user, as a larger invoked-service intersection often means higher probability that two users are similar friends (i.e., neighbors). However, the above similar-neighbor finding process is often time-consuming and vulnerable to privacy, especially when the number of candidate users is huge. In view of these challenges, a scalable and privacy-preserving similar-neighbor finding approach based on SimHash, i.e., SNF SimHash is proposed in this paper. Finally, a set of experiments are conducted on WS-DREAM dataset to validate the feasibility of SNF SimHash . Experiment results show that our proposal can achieve a good tradeoff between accuracy and efficiency while guaranteeing privacy-preservation." @default.
- W2765866812 created "2017-11-10" @default.
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- W2765866812 date "2017-01-01" @default.
- W2765866812 modified "2023-09-27" @default.
- W2765866812 title "SimHash-Based Similar Neighbor Finding for Scalable and Privacy-Preserving Service Recommendation" @default.
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- W2765866812 doi "https://doi.org/10.1007/978-3-319-68542-7_10" @default.
- W2765866812 hasPublicationYear "2017" @default.
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