A systematic review and research perspective on recommendation systems

Author(s)	Tanuja Taware
Country	India
Abstract	User preferences enable recommendation engines to produce personalized behavior patterns which facilitate user show. The discussion focuses on main filtering strategies which include collaborative and content-based techniques followed by hybrid approaches. methods, matrix factorization, association rule mining, and deep learning. Each method obtains a detailed evaluation regarding its specific advantages and disadvantages while the amalgamation of different methods leads to better accuracy and user satisfaction. improves accuracy and user satisfaction.
Keywords	Recommendation Systems, Collaborative Filtering, Content-Based Filtering, Hybrid Methods, Cold-Start Problem, Data Sparsity, Scalability, User Satisfaction
Field	Computer Applications
Published In	Volume 16, Issue 2, April-June 2025
Published On	2025-06-28
DOI	https://doi.org/10.71097/IJSAT.v16.i2.6422
Short DOI	https://doi.org/g9r8gc

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International Journal on Science and Technology