Enhancing the Role of Large-Scale Recommendation Systems in the IoT Context

Abstract

The Internet of Things (IoT) connects heterogeneous physical devices with the ability to collect data using sensors and actuators. These data can infer useful information for decision-makers in many applications systems monitoring, healthcare, transportations, data storage, smart homes, and many others. In the Era of the Internet of Things (IoT), recommender systems can support scenarios such as recommending apps, IoT workflows, services, sensor equipment, hotels, and drugs to users and customers. Current state-of-art recommendation systems, including collaborative filtering methods, suffer from scalability and sparsity problems. This article proposes a clustering-based recommendation system that adopts the vector space model from information retrieval to obtain highly accurate recommendations. The proposed algorithm uses four well-known clustering techniques as k-means (KM), fuzzy c-mean (FCM), Single-Linkage (SLINK), and Self-Organizing-Maps (SOM). Various experiments and benchmarking on seven IoT rating datasets from different fields are conducted to assess the performance of the proposed recommender system. The experimental results using both error and prediction metrics indicate that the proposed algorithm outperforms the traditional collaborative filtering approach. Besides, adopting the self-organizing strategy obtains recommendations of significant accuracy as compared to the partitional learning approaches.