An appropriate and cost-effective hospital recommender system for a patient of rural area using deep reinforcement learning

Jha, Rajesh Kumar; Bag, Sujoy; Koley, Debbani; Bojja, Giridhar Reddy; Barman, Subhas

doi:10.1016/j.iswa.2023.200218

Intelligent Systems with Applications

2023

DOI: 10.1016/j.iswa.2023.200218

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An appropriate and cost-effective hospital recommender system for a patient of rural area using deep reinforcement learning

Rajesh Kumar Jha¹,

Sujoy Bag²,

Debbani Koley³

et al.

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2024

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“…Recently, a new research trend in recommendation research is the use of RL to address recommendation problems 4 . In practice, RL-based RSs have been used for a variety of specialized applications, including movie recommendations 21 , news recommendations 22 , treatment recommendations 23,24 , opinion analysis 25 , and e-learning 26,27 .…”

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confidence: 99%

An adaptable and personalized framework for top-N course recommendations in online learning

Amin,

Uddin,

Alarood

et al. 2024

Sci Rep

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In recent years, the proliferation of Massive Open Online Courses (MOOC) platforms on a global scale has been remarkable. Learners can now meet their learning demands with the help of MOOC. However, learners might not understand the course material well if they have access to a lot of information due to their inadequate expertise and cognitive ability. Personalized Recommender Systems (RSs), a cutting-edge technology, can assist in addressing this issue. It greatly increases resource acquisition through personalized availability for various people of all ages. Intelligent learning methods, such as machine learning and Reinforcement Learning (RL) can be used in RS challenges. However, machine learning needs supervised data and classical RL is not suitable for multi-task recommendations in online learning platforms. To address these challenges, the proposed framework integrates a Deep Reinforcement Learning (DRL) and multi-agent approach. This adaptive system personalizes the learning experience by considering key factors such as learner sentiments, learning style, preferences, competency, and adaptive difficulty levels. We formulate the interactive RS problem using a DRL-based Actor-Critic model named DRR, treating recommendations as a sequential decision-making process. The DRR enables the system to provide top-N course recommendations and personalized learning paths, enriching the student's experience. Extensive experiments on a MOOC dataset such as the 100 K Coursera course review validate the proposed DRR model, demonstrating its superiority over baseline models in major evaluation metrics for long-term recommendations. The outcomes of this research contribute to the field of e-learning technology, guiding the design and implementation of course RSs, to facilitate personalized and relevant recommendations for online learning students.

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An adaptable and personalized framework for top-N course recommendations in online learning

Amin,

Uddin,

Alarood

et al. 2024

Sci Rep

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An appropriate and cost-effective hospital recommender system for a patient of rural area using deep reinforcement learning

Cited by 1 publication

References 21 publications

An adaptable and personalized framework for top-N course recommendations in online learning

An adaptable and personalized framework for top-N course recommendations in online learning

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