Ensuring privacy and correlation awareness in multi-dimensional service quality prediction and recommendation for IoT

Abstract

Edge computing, with its advantages in terms of lightweight data transmission between users and cloud platforms, has become a promising solution for alleviating the heavy burden of timely data processing in many IoT scenarios, such as smart commerce and smart healthcare. However, several challenges arise when fusing multi-source IoT data recorded by different edge servers. First of all, data repetition within each edge server can greatly reduce the efficiency of various edge-based smart applications. Besides, IoT data fusion associated with multiple distributed edge servers can compromise user privacy. In addition, the multi-dimensional and interrelated nature of IoT data complicates precise data mining and analysis. To tackle these issues, a novel edge data fusion method (named TLTM) for cross-platform service recommendation is brought forth, which considers data dimensions, data correlation, and data privacy simultaneously. Finally, to validate the effectiveness and efficiency of the TLTM method, we have designed extensive experiments on the popular WS-DREAM dataset. The reported experimental results show that our TLTM method is superior to other related methods in terms of popular performance metrics including MAE, RMSE, Precision, Recall, F1-Score, and Time cost.