Abstract
IoT service recommendation techniques can help a user select appropriate IoT services efficiently. Aiming at improving the recommendation efficiency and preserving the data privacy, the locality-sensitive hashing (LSH) technique is adopted in service recommendation. However, existing LSH-based service recommendation methods ignore the intrinsic temporal feature of IoT services. In light of this challenge, we integrate the temporal feature into the conventional LSH-based method and present a time-aware approach with the capability of privacy preservation for IoT service recommendation across multiple platforms. Experiments on a real-world dataset are conducted to validate the advantage of our proposed approach in terms of accuracy and efficiency in recommendation.
Highlights
Introduction e rapidly increasing number ofIoT devices and services are continuously producing a vast amount of data
All of the abovementioned methods employed the time factor to enhance the performance of recommendation system; none of those methods take privacy preservation into account, which is necessary when quality of service (QoS) data is collected from different platform to obtain more comprehensive user preference
As a user may invoke IoT service from different platforms, his/her historical QoS data is stored across multiple platforms
Summary
We review the related research work on timeaware IoT services recommendation with privacy preservation from the following three aspects. All of the abovementioned methods employed the time factor to enhance the performance of recommendation system; none of those methods take privacy preservation into account, which is necessary when QoS data is collected from different platform to obtain more comprehensive user preference. In [20], Ma et al proposed K-anonymity method to protect user privacy through hiding sensitive user identification information This may influence the data availability and decrease the performance of recommendation systems . As a conclusion, existing IoT service recommendation methods fail in taking time factor and privacy preservation into account simultaneously [22,23,24,25,26]. In light of this challenge, we improve the conventional LSH method and present a time-aware cross-platform IoT service recommendation algorithm with privacy preservation
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