Abstract
Recommender systems anticipate customers' present preferences for certain items based on their past behavior, offering individualized service support to users. The evolution of recommender systems has naturally included artificial intelligence (AI), notably computational intelligence and machine learning techniques and algorithms to enhance prediction precision and resolve issues with cold start and data sparsity. This paper methodically examines the fundamental approaches and current practices in recommender systems.
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More From: International Journal of Engineering Science and Humanities
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