A Brief Model Overview of Personalized Recommendation to Citizens in the Health‐Care Industry
Subhasish Mohapatra, Kunal Anand · 2020
The Recommender System is one of the aspiring domains in today's information and communication technology world. The recommender system does a metric analysis and determine if the recommended product is relevant or not for a user. A recommender system analyzes a large amount of unstructured data for intelligent mining. In recommender system, the procedure of delivering an explanation is based on an intelligent collaborative filtering process for decision making. In this context, the collaborative filtering-based recommendation system model has become an indispensable tool for future decision insemination to end-user. The recommender system avails valuable information from citizens, doctors, pharmacists, and market analysts to create a datastore where the decision logic and collaborative filtering can be applied to disseminate recommendation to the citizens for the service they need in due course of time. This chapter provides an insight into the implementation of the recommender system in both tangible and non-tangible products as well as the service care industry. With the implementation of this recommender system model, the remedial solution for citizens will gain a new momentum. The recent research needs a large volume of products or medical data to ensure its quality and trustworthiness. By the advancement of research, this model will derive valuable outcome. This model is a context-specific recommendation system that will grow further after data collection and learning. In this chapter, we provide the detailed theoretical framework of the flow chart-based recommender system in the health care model that strongly supports transparency, trustworthiness, and validity.