A new service recommendation method for agricultural industries in the fog-based Internet of Things environment using a hybrid meta-heuristic algorithm

Abstract

Regardless of public perceptions of the agricultural techniques, the fact is that the current agriculture industry is more data-driven, accurate, and intelligent than before. Therefore, novel technologies such as fog computing and the Internet of Things (IoT) can provide a flexible and real-time platform to meet the data-driven requirements of the current agricultural decision-makers. Smart agriculture is a new idea since IoT sensors and fog platforms can provide information about agriculture and then operate on them based on user feedback. Also, with the rapid growth of IoT, the importance of recommender systems is increased in this domain. Therefore, the main goals of this study are to improve the accuracy of agricultural service recommendations and decrease the Mean Absolute Error (MAE) in the IoT-based fog systems using collaborative filtering and artificial Artificial Bee Colony (ABC). However, many of the current methods suffer from low accuracy of recommendations of the agricultural services. The present article suggested a collaborative filtering-based approach based on the ABC and genetic operators to design an effective recommender scheme in fog-based IoT systems. The results showed that the accuracy and MAE are optimized compared to some state-of-the-art methods. They also revealed that compared to other methods, the proposed method improves ranking score by 5.8 %, precision by 5%, recall by 5.7%, intra similarity by 13%, and hamming distance by 4.8%.