Time-Aware Cross-Platform IoT Service Recommendation with Privacy Preservation

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.