The rapid technological advancement in E-tail has led to an overwhelming influx of data on the internet. Everyday decisions such as purchasing products, selecting music, reading news, and choosing movies are common, and people often rely on input from friends and family for these choices. One of the oldest forms of recommendation is “word-of-mouth,” where individuals seek suggestions from others, especially when buying products. Although relatively new compared to other information systems, Recommender Systems play a crucial role in addressing the challenge of sifting through extensive, unstructured data to help users make optimal selections. These systems simplify the process of choosing items, products, or information from a vast web-based collection, utilizing data mining techniques and predictive algorithms to offer recommendations. Over the past few decades, the internet’s data volume has grown exponentially, prompting the development of various technologically advanced approaches to assist users in finding relevant information. This study aims to organize these approaches effectively to provide high-quality recommendations. Specifically focusing on the healthcare industry, this study offers a comprehensive overview of how recommender systems are being utilized. In modern times, these systems are extensively employed in the healthcare sector to enhance patient care and aid medical professionals in decision-making. It’s worth noting that creating recommendation techniques in the healthcare field requires addressing unique requirements compared to other domains. This chapter aims at providing a comprehensive overview on recommender systems, categories of recommender methods among knowledge-based, hybrid techniques, content-based, collaborative, and demographic and application scenarios. The intended readership of this chapter encompasses researchers, professionals, academics, as well as undergraduate and postgraduate students who are interested in gaining a deeper understanding of recommender systems in the healthcare sector.

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Application Scenarios of Recommender System in Healthcare

  • B. S. Shruthi,
  • K. B. Manasa,
  • J. Sudeep

摘要

The rapid technological advancement in E-tail has led to an overwhelming influx of data on the internet. Everyday decisions such as purchasing products, selecting music, reading news, and choosing movies are common, and people often rely on input from friends and family for these choices. One of the oldest forms of recommendation is “word-of-mouth,” where individuals seek suggestions from others, especially when buying products. Although relatively new compared to other information systems, Recommender Systems play a crucial role in addressing the challenge of sifting through extensive, unstructured data to help users make optimal selections. These systems simplify the process of choosing items, products, or information from a vast web-based collection, utilizing data mining techniques and predictive algorithms to offer recommendations. Over the past few decades, the internet’s data volume has grown exponentially, prompting the development of various technologically advanced approaches to assist users in finding relevant information. This study aims to organize these approaches effectively to provide high-quality recommendations. Specifically focusing on the healthcare industry, this study offers a comprehensive overview of how recommender systems are being utilized. In modern times, these systems are extensively employed in the healthcare sector to enhance patient care and aid medical professionals in decision-making. It’s worth noting that creating recommendation techniques in the healthcare field requires addressing unique requirements compared to other domains. This chapter aims at providing a comprehensive overview on recommender systems, categories of recommender methods among knowledge-based, hybrid techniques, content-based, collaborative, and demographic and application scenarios. The intended readership of this chapter encompasses researchers, professionals, academics, as well as undergraduate and postgraduate students who are interested in gaining a deeper understanding of recommender systems in the healthcare sector.