Abstract

Recommender systems make individualized suggestions for users based on their actions and preferences by utilizing machine learning (ML) and artificial intelligence (AI).. These systems have evolved significantly, incorporating various AI techniques like fuzzy techniques, transfer learning, genetic algorithms, neural networks, deep learning, and more. The use of AI in recommender systems aims to enhance prediction accuracy and address data sparsity issues.1 Key methodologies in recommender systems include deep neural networks, transfer learning, active learning, fuzzy techniques, evolutionary algorithms, natural language processing, and computer vision.1 These techniques play crucial roles in knowledge representation, reasoning, planning, communication, perception, and image processing within recommender systems.1 Machine Learning plays a vital role in recommendation systems by utilizing algorithms like KNN clustering, Naive Bayes, collaborative filtering and content filtering to suggest products to users overwhelmed by information on e-commerce platforms.4 Additionally, Recommender Systems (RSs) are widely used across various domains such as ecommerce, tourism, health, and e-learning to enhance user experience and increase sales through personalized recommendations based on user preferences.5 RSs have become integral in guiding decisions for users in online transactions and improving the quality of their interactions with platforms like Amazon, Netflix, YouTube, Spotify, Facebook, and Twitter5 used to pinpoint people who are most at risk for developing complications from an illness or who are most likely to have poor treatment outcomes. These data can be used to develop personalized treatment plans for patients.