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Review of Modern Recommendation Systems


EOI: 10.11242/viva-tech.01.09.09

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Citation

Mithilesh Vichare, Suraj Maurya, Brijesh Gautam, Prof. Kirtida Naik, "Review of Modern Recommendation Systems ", VIVA-IJRI Volume 1, Issue 9, Article 19, pp. 1-6, 2026. Published by Computer Engineering Department, VIVA Institute of Technology, Virar, India.

Abstract

With the exponential growth of digital content on the Internet, users are often overwhelmed by the vast amount of available information. This problem, commonly known as information overload, makes it difficult for users to identify items that match their preferences. Recommender systems have emerged as an effective solution by providing personalized suggestions based on user interests and behavior. This review paper presents a comprehensive study of recommender systems with a focus on content-based filtering, collaborative filtering, and hybrid recommender systems. The working principles, advantages, limitations, and real-world applications of each approach are discussed in detail. Furthermore, the paper highlights how hybrid recommender systems combine multiple techniques to overcome the weaknesses of individual approaches and improve recommendation accuracy and reliability.

Keywords

- Collaborative Filtering, Content-Based Filtering, Hybrid Model, Machine Learning, Personalization, Recommendation Systems, Similarity Analysis.

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