IJARSAI INTERNATIONAL JOURNAL OF ADVANCED RESEARCH SCIENTIFIC ANALYSIS & INFERENCES
UPID No: 1125P2
Enhancing Personalization in Recommendation Systems: A Comparative Study of Machine Learning Models
Article Title: Enhancing Personalization in Recommendation Systems: A Comparative Study of Machine Learning Models
Author(s): Praseetha M.S. , Assistant Professor, Department of Computer Applications, SCMS School of Technology and Management, Kochi, Kerala, Email: praseetha@scmsgroup.org
Tania Shine, Scholar, SCMS School of Technology and Management, Kochi, Kerala, Email: taniashine06@gmail.com
Discipline: Computer Science
Paper Information: Received: 05 Aug 2024| Revised Submission: 10 Sep 2024 | Accepted: 05 Dec 2024| Available Online: 15 Mar 2025
DOI:
Abstract: In this research paper the authors try to present a comprehensive study on the development and evaluation of a personalized content recommendation system for the Netflix platform. Various machine learning models were implemented and compared usingmetrics such as accuracy, recall, F1-score, and precision, to enhance the accuracy and effectiveness of content recommendations. The study encompasses various machine learning techniques, including TF-IDF, Cosine Similarity, Support Vector Machines (SVM), K Nearest Neighbors (KNN), and Nearest Neighbors, to build a hybrid model. The study provides valuable insights into the strengths and limitations of different models applied to the recommendation systems and also enhances personalization by the utilization of hybrid models.
Keywords: Machine Learning, Recommendation Systems, Natural Language Processing, Cosine Similarity, TF-IDF, Support Vector Machines, Comparative Study, Netflix Data.
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