Textual Online Content Recommendation Towards Accountability: A Thorough Disquisition of Recent Advances, Problems, and Potential Scope Anatomizing Obscure Features.
Keywords:
practitioners, ultimately, recommendation, mitigating,Abstract
The vast expanse of online information often overwhelms users, leaving them unable to navigate through the wealth of available data. Recommender systems have emerged as a remedy to aid users by suggesting relevant items based on their preferences. However, many challenges persisted, hampering these systems. This paper conducts a thorough literature review, delving into various approaches aimed at mitigating issues in recommender systems. It explores diverse similarity measures, strategies within different recommender systems, and the use of multimodal data. Additionally, the study evaluates the effectiveness of these approaches in tackling the challenges focusing on evaluation metric utilities. The findings underscore the significance of addressing sparsity, providing valuable insights for researchers and practitioners to develop more robust recommender systems, ultimately enhancing recommendation accuracy and efficiency.
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