A novel distributed Social Internet of Things service recommendation scheme based on LSH forest

For the Social Internet of Things (SIoT), the interaction among ever increasing number of smart devices results in an exponential increase of services, which leads to an extreme difficulty for users to find suitable services. To address this issue, most existing recommendation algorithms are based on the data stored on the centralized server and distributed schemes are ignored. Meanwhile, distributed recommendation algorithms face the problems of privacy leakage and efficiency, which decrease the quality of experience (QoE). Therefore, we propose a novel SIoT service recommendation scheme called SIoT- SR, which adopts LSH Forest while combining with collaborative filtering algorithm to predict the Quality of Service (QoS) data of users. The LSH forest implements binary search by sorting and also has the ability to self-correct parameters. It can achieve a good tradeoff among memory, accuracy, efficiency, and privacy. Finally, we validate the effectiveness of the scheme based on the dataset WS-DREAM. The experimental results show that SIoT-SR has high prediction accuracy and efficiency while saving computing resources and are suitable for service recommendation of SIoT with resource-constrained devices.