Research Article
Privacy-Preserving and Scalable Service Recommendation Based on SimHash in a Distributed Cloud Environment
Box 1
Four steps of service recommendation approach
.Step 1 (buliding user indexes offline based on SimHash). For each user , calculate | his/her hash value offline based on SimHash. Then is regarded as the index | for . | Step 2 (finding “probably similar” friends of the target user). According to the same hash | function adopted in Step 1, calculate user index for , that is, . If the Hamming | Distance between and is smaller than 3, then is considered as a | “probably similar” friend of . | Step 3 (finding “really similar” friends of the target user). For a “probably similar” friend | obtained in Step 2, calculate his/her similarity with ; if the similarity is larger than a | threshold , then is a “really similar” friend of . | Step 4 (service recommendation). According to ’s “really similar” friends derived in | Step 3, predict the quality of services never invoked by and recommend the | quality-optimal services to . |
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