Juice Recipe Recommendation System Using Machine Learning in MEC Environment

Abstract

The combination of machine learning with healthcare services is emerging. However, when machine learning functions are executed on resource-constrained mobile consumer devices, the computing overhead will increase, and the user experience will deteriorate. In essence, the existing approaches delegate such tasks to a central cloud data center. However, data processing on remote cloud servers results in a long response latency. Therefore, this article proposes a machine learning-based smart recipe recommendation system that uses a local mobile edge computing server. The proposed system delegates the machine learning and recipe search tasks to the mobile edge computing server, thereby reducing the response latency for data processing and the computational burden placed on mobile user devices.