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Robust and Privacy-Preserving Service Recommendation over Sparse Data in Education
Joint Authors
Liu, Hanwen
Yan, Chao
Xu, Yanwei
Chen, Xuening
Source
Wireless Communications and Mobile Computing
Issue
Vol. 2019, Issue 2019 (31 Dec. 2019), pp.1-13, 13 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2019-06-20
Country of Publication
Egypt
No. of Pages
13
Main Subjects
Information Technology and Computer Science
Abstract EN
Service recommendation has become one of the most effective approaches to quickly extract insightful information from big educational data.
However, the sparsity of educational service quality data (from multiple platforms or parties) used to make service recommendations often leads to few even null recommended results.
Moreover, to protect sensitive business information and obey laws, preserving user privacy during the abovementioned multisource data integration process is a very important but challenging requirement.
Considering the above challenges, this paper integrates Locality-Sensitive Hashing (LSH) with hybrid Collaborative Filtering (HCF) techniques for robust and privacy-aware data sharing between different platforms involved in the cross-platform service recommendation process.
Furthermore, to minimize the “False negative” recommended results incurred by LSH and enhance the success of recommended results, we propose two optimization strategies to reduce the probability that similar neighbours of a target user or similar services of a target service are overlooked by mistake.
Finally, we conduct a set of experiments based on a real distributed service quality dataset, i.e., WS-DREAM, to validate the feasibility and advantages of our proposed recommendation approach.
The extensive experimental results show that our proposal performs better than three competitive methods in terms of efficiency, accuracy, and successful rate while guaranteeing privacy-preservation.
American Psychological Association (APA)
Chen, Xuening& Liu, Hanwen& Xu, Yanwei& Yan, Chao. 2019. Robust and Privacy-Preserving Service Recommendation over Sparse Data in Education. Wireless Communications and Mobile Computing،Vol. 2019, no. 2019, pp.1-13.
https://search.emarefa.net/detail/BIM-1212033
Modern Language Association (MLA)
Chen, Xuening…[et al.]. Robust and Privacy-Preserving Service Recommendation over Sparse Data in Education. Wireless Communications and Mobile Computing No. 2019 (2019), pp.1-13.
https://search.emarefa.net/detail/BIM-1212033
American Medical Association (AMA)
Chen, Xuening& Liu, Hanwen& Xu, Yanwei& Yan, Chao. Robust and Privacy-Preserving Service Recommendation over Sparse Data in Education. Wireless Communications and Mobile Computing. 2019. Vol. 2019, no. 2019, pp.1-13.
https://search.emarefa.net/detail/BIM-1212033
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1212033