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Lianyong QI, Weiyi ZHONG, Chunhua HU, et al., “Correlation-aware Multi-dimensional Service Quality Prediction and Recommendation with Privacy-preservation in IoT,” Chinese Journal of Electronics, vol. x, no. x, article no. , xxxx doi: 10.23919/cje.2023.00.112
Citation: Lianyong QI, Weiyi ZHONG, Chunhua HU, et al., “Correlation-aware Multi-dimensional Service Quality Prediction and Recommendation with Privacy-preservation in IoT,” Chinese Journal of Electronics, vol. x, no. x, article no. , xxxx doi: 10.23919/cje.2023.00.112

Correlation-aware Multi-dimensional Service Quality Prediction and Recommendation with Privacy-preservation in IoT

doi: 10.23919/cje.2023.00.112
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  • Author Bio:

    Lianyong QI was born in 1982, he received the Ph.D. degree in computer science and technology from University of Nanjing, China, in 2011. He is now a professor of the School of Computer Science, Qufu Normal University, China. His recent research interests include service computing. (Email: lianyongqi@gmail.com)

    Weiyi ZHONG was born in 1995. She received the masters degree from the School of Computer Science, Qufu Normal University, Rizhao, China, in 2021. She is currently a Ph.D. Candidate with the College of engineering, Qufu Normal University. Her inerests include recommender systems and services computing. (Email:weiyizhong999@gmail.com)

    Chunhua HU was born in 1973, he received the Ph.D. degree in computer science from Central South University, Changsha, China, in 2007. He is now a Professor with the Institute with Big Data and Internet Innovation, Hunan University of Technology and Business, Changsha. His research interests include big data analysis, artificial intelligence, and personalized recommendation (Email: huchunhua777@163.com)

    Xiaokang ZHOU was born in 1984, he received the Ph.D. degree in human sciences from Waseda University, Tokyo, Japan. He is now an Associate Professor of Shiga University of Faculty of Data Science, Japan. His recent research interests include ubiquitous computing, big data, machine learning, cyber–physical–social system, cyber intelligence. (Email: zhou@biwako.shiga-u.ac.jp)

    Fan WANG was born in 1997, She received the master’s degree from the School of Computer Science, Qufu Normal University, Rizhao, China, in 2021. She is currently pursuing a PhD degree at the College of Computer Science and Technology, Zhejiang University, Hangzhou, P.R. China. Her research interests include big data analyses and recommendation system. (Email: fanwang1997@gmail.com)

    Yuwen LIU was born in 1997, she received the master’s degree from the School of Computer Science, Qufu Normal University Rizhao, China, in 2022. She is currently a Ph.D. Candidate with the College of Computer Science and Technology, China University of Petroleum (East China). Her research interests include recommender systems, privacy protection. (Email:yuwenliu97@gmail.com)

    Chao YAN was born in 1982, he received the master’s degree from Chinese Academy of Sciences, China in 2006. He is currently a PhD candidate at Shandong University of Science and Technology, Qingdao, China. His research interests include recommender system and service computing. (Email: yanchao@qfnu.edu.cn.)

  • Corresponding author: Email: yanchao@qfnu.edu.cn
  • Received Date: 2023-03-22
  • Accepted Date: 2023-03-22
  • Available Online: 2023-03-22
  • Benefiting from the low data transmission requirements from user clients to remote cloud centers, edge computing has emerged as a lightweight and cost-effective solution for various data-intensive IoT applications, including intelligent transportation and smart healthcare. However, integrating distributed IoT data from multiple edge servers to provide better services poses practical and valuable research challenges. First, data redundancy is possible in each edge server, which reduces IoT data processing and transmission efficiency significantly. Second, user privacy is probably breached when the IoT data stored in different edge servers are integrated together for comprehensive data analysis and mining. Third, IoT data are often multi-dimensional and correlated with each other, which places an obstacle to scientific and accurate data analysis and decision-making. To solve these challenges, we propose a multi-dimensional and correlation-aware service quality prediction and recommendation approach with privacy preservation for edge-assisted IoT applications, named TLTM. Specifically, our approach employs Truncated Singular Value Decomposition (TSVD) to remove data redundancy in each edge server, Locality-Sensitive Hashing (LSH) to secure user privacy during multi-source data integration, and Mahalanobis distance to minimize correlation among different data dimensions. Finally, the feasibility of our proposal is validated through experiments conducted on the well-known WS-DREAM dataset.
  • 1https://wsdream.github.io/
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