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Privacy‐Aware Multidimensional Mobile Service Quality Prediction and Recommendation in Distributed Fog Environment
Open accessAbstract
With the ever‐increasing popularity of mobile computing technology, a wide range of computational resources or services (e.g., movies, food, and places of interest) are migrating to the mobile infrastructure or devices (e.g., mobile phones, PDA, and smart watches), imposing heavy burdens on the service selection decisions of users. In this situation, service recommendation has become one of the promising ways to alleviate such burdens. In general, the service usage data used to make service recommendation are produced by various mobile devices and collected by distributed edge platforms, which leads to potential leakage of user privacy during the subsequent cross‐platform data collaboration and service recommendation process. Locality‐Sensitive Hashing (LSH) technique has recently been introduced to realize the privacy‐preserving distributed service recommendation. However, existing LSH‐based recommendation approaches often consider only one quality dimension of services, without considering the multidimensional recommendation scenarios that are more complex but more common. In view of this drawback, we improve the traditional LSH and put forward a novel LSH‐based service recommendation approach named SerRec multi-qos, to protect users’ privacy over multiple quality dimensions during the distributed mobile service recommendation process.
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Metadata
- Authors
- Gong, W., Qi, L., & Xu, Y.
- Affiliations
- Qufu Normal University (CN)
- Corresponding author
- Qi, Lianyong
- Venue
- Wireless Communications and Mobile Computing
- Publisher
- Wiley
- Published
- 2018-01
- Volume
- 2018
- Issue
- 1
- ISSN
- 1530-8669 (ISSN-L)
- 1530-8677
- OpenAlex
- W2799796316
- Cited by
- 166
- References
- 46
- Citations by year
- 2025: 1 · 2023: 9 · 2022: 11 · 2021: 16 · 2020: 71 · 2019: 50 · 2018: 8
- FWCI
- 58.47
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Locations
- Wireless Communications and Mobile Computing (publishedVersion, cc-by)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- Open Project of State Key Laboratory for Novel Software Technology (KFKT2016B22)
- Topics
- Physical Sciences › Computer Science › Information Systems › Recommender Systems and Techniques
- Physical Sciences › Computer Science › Artificial Intelligence › Privacy-Preserving Technologies in Data
- Physical Sciences › Computer Science › Information Systems › Service-Oriented Architecture and Web Services
- Keywords
- privacy-preserving recommendation, quality of service prediction, locality-sensitive hashing, user privacy
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