№ 60-3 (том 1): ОБРАЗОВАНИЕ И НАУКА В XXI ВЕКЕ, Март, 2025
Научно-образовательные статьи

COMPARISON OF COLLABORATIVE FILTERING, ITEM BASED AND CONTENT BASED RECOMMENDATION SYSTEMS

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  • Dowran Myradov Myradovich,
  • Kakyshov Eziz Begdurdyyevich
Dowran Myradov Myradovich
Oguz han Engineering and Technology university of Turkmenistan. Ashgabat, Turkmenistan
Kakyshov Eziz Begdurdyyevich
Oguz han Engineering and Technology university of Turkmenistan. Ashgabat, Turkmenistan

Опубликован 26.03.2025

Как цитировать

M.M. Dowran, & E.B. Kakyshov. (2025). COMPARISON OF COLLABORATIVE FILTERING, ITEM BASED AND CONTENT BASED RECOMMENDATION SYSTEMS. ОБРАЗОВАНИЕ И НАУКА В XXI ВЕКЕ, 60-3 (том 1). https://mpcareer-google.ru/index.php/journal/article/view/1301

Аннотация

Comparison of Collaborative Filtering, Item-Based, and Content-Based Recommendation Systems

Introduction

Recommendation systems are critical for personalized user experiences in platforms like Netflix, Amazon, and Spotify. Three dominant approaches—**collaborative filtering (CF)**, **item-based filtering**, and **content-based filtering**—differ in methodology, strengths, and limitations. This article compares these systems across key dimensions, including data requirements, scalability, and real-world applications. 

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Библиографические ссылки

  1. Koren, Y., Bell, R., & Volinsky, C. (2009). "Matrix Factorization Techniques for Recommender Systems." *IEEE Computer*.
  2. Ricci, F., et al. (2015). *Recommender Systems Handbook*. Springer.
  3. Lops, P., et al. (2011). "Content-Based Recommender Systems: State of the Art and Trends." *Recommender Systems Handbook*.
  4. Amazon Science. (2020). "How Amazon’s Recommendation Engine Works."
  5. Netflix Tech Blog. (2022). "Personalization at Netflix."