An efficient periodic web content recommendation based on web usage mining

Khatri, Ravi; Gupta, Daya

doi:10.1109/retis.2015.7232866

Cited by 3 publications

References 10 publications

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RETRACTED: Evolution from ancient medication to human‐centered Healthcare 4.0: A review on health care recommender systems

Sharma

Aujla

Bajaj

2019

Int J Communication

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The evolution of intelligent and data-driven systems has pushed for the tectonic transition from ancient medication to human-centric Healthcare 4.0. The rise of Internet of Things, Internet of Systems, and wireless body area networks has endowed the health care ecosystem with a new digital transformation supported by sophisticated machine learning and artificial intelligence algorithms. Under this umbrella, health care recommendation systems have emerged as a driver for providing patient-centric personalized health care services. Recommendation systems are automatic systems that derive the decisions on the basis of some valid input parameters and vital health information collected through wearable devices, implantable equipments, and various sensor. Therefore, to understand the state-of-the-art developments in the health care ecosystem, this paper provides a comprehensive survey on health care recommendation systems and the associated paradigms. This survey starts from the ancient health care era and move toward the Healthcare 4.0 in a phased manner. The road map from Healthcare 1.0 to Healthcare 4.0 is analyzed to highlight different technology verticals supporting the digital transformation. This study also provides the systematic review of the health care systems, the types of health care systems, and the recommender systems. Moreover, a deep analysis of health care recommender systems and its types is also presented. Finally, the open issues and challenges associated with the adaption and implementation of human-centric Healthcare 4.0 ecosystem are discussed. This is provided to find out the possible research questions and gaps so that the corresponding solutions could be developed in the near future. KEYWORDScollaborative filtering-based recommender systems, content-based recommender systems, context-aware recommender system, health care recommender system, Healthcare 4.0, human-centric systems INTRODUCTIONThe increased demand of the health statistics and the variations in looking for information tactics can be pragmatic around the globe. 1 As per the latest study, 81% of the adults in the United States utilize the Internet services, and out of them, 59%

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