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A Comprehensive Analysis of Context-aware Recommender Systems Implemented Across a Range of Domains

Muhammad Nadeem, Syeda Wajiha Zahra, Muhammad Nouman Abbasi, Ali Arshad, Saman Riaz, Waqas Ahmed

Abstract


In many different fields, recommender systems are essential for helping users find pertinent and personalized material. Traditional recommender systems often underestimate the impact of contextual factors on user preferences. Context-aware recommender systems are a potential method that uses dynamic contextual data to increase recommendation accuracy and user satisfaction. This review paper examines the benefits of context-aware recommender systems in many fields and offers a detailed analysis of these systems. We also discuss the difficulties and outstanding research issues related to context-aware recommender systems. These difficulties include problems with scalability, context representation, data sparsity, and privacy issues. The knowledge presented in this review aims to stimulate more developments and improvements in the field, producing more efficient and individualized recommendation systems in various domains.


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References


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