Location-aware service recommendations with privacy-preservation in the Internet of Things

Wenmin Lin, Xuyun Zhang, Lianyong Qi, Weimin Li, Shancang Li, Victor S. Sheng, Surya Nepal

Research output: Contribution to journalArticlepeer-review

20 Citations (Scopus)
96 Downloads (Pure)


With the ever-increasing maturity and popularization of the Internet of Things (IoT), tremendous business applications developed by distinct enterprises or organizations have been encapsulated into lightweight web services that can easily be accessed or invoked remotely. However, the big volume of candidate web services places a heavy burden on the users' service selection decision-making process. Under the circumstance, a variety of intelligent recommendation solutions have been developed to reduce the high decision-making cost. Traditional resolutions usually challenge in two aspects. First, the recommendation parameters, i.e., the quality of services (QoS), usually relies on user/service location heavily; therefore, low-quality recommended results may be returned to users if user/service location information is overlooked. Second, historical QoS data often contain partial sensitive information of users; therefore, it becomes a necessity to protect the sensitive QoS data while making accurate recommendation decisions. To tackle the above challenges, we introduce the concepts of user/service location information and locality-sensitive hashing (LSH) in the domain and propose a location-aware recommendation approach with privacy-preservation capability. A wide range of experiments is set up based on the popular WS-DREAM data set, whose results prove the effectiveness and efficiency of our approach.
Original languageEnglish
Pages (from-to)227-235
Number of pages9
JournalIEEE Transactions on Computational Social Systems
Issue number1
Early online date4 Feb 2020
Publication statusPublished - Feb 2021


  • Locality-sensitive hashing (LSH)
  • location
  • privacy
  • quality of service (QoS)
  • recommender system.
  • recommender system


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