Amplified locality‐sensitive hashing‐based recommender systems with privacy protection

Summary With the advent of Internet of Things (IoT) age, the variety and volume of web services have been increasing at a fast speed. This often leads to users' selections for web services more complicated. Under the circumstance, a variety of methods such as collaborative filtering are adopted...

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Bibliographic Details
Published in:Concurrency and computation 2022-06, Vol.34 (14), p.n/a
Main Authors: Chi, Xiaoxiao, Yan, Chao, Wang, Hao, Rafique, Wajid, Qi, Lianyong
Format: Article
Language:English
Subjects:
Amplification
amplified LSH
Collaboration
collaborative filtering
Computing time
Data communication
distributed service recommendation
efficiency
Filtration
Internet of Things
Platforms
Privacy
Recommender systems
Web services
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Summary:Summary With the advent of Internet of Things (IoT) age, the variety and volume of web services have been increasing at a fast speed. This often leads to users' selections for web services more complicated. Under the circumstance, a variety of methods such as collaborative filtering are adopted to deal with this challenging situation. While traditional collaborative filtering method has some shortcomings, one of which is that only centralized user‐service data are considered while distributed quality data from multiple platform are ignored. Generally, service recommendation across different platforms often involves data communication among multiple platforms, during which user privacy may be disclosed and much computational time is required. Considering these challenges, a unique amplified locality‐sensitive hashing (LSH)‐based service recommendation method, that is, SRAmplified‐LSH, is proposed in the article. SRAmplified‐LSH can guarantee a good balance between accuracy and efficiency of recommendation and user privacy information. Finally, extensive experiments deployed on WS‐DREAM dataset validate the feasibility of our proposed method.
ISSN:1532-0626
1532-0634
DOI:10.1002/cpe.5681