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Journal of Information and Optimization Sciences

(Since 1980)
Hybrid ·Peer-reviewed·ISSN (Online): 2169-0103·ISSN (Print): 0252-2667

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Monthly Journal: Publishes theoretical and applied research on topics in information and optimization sciences.

Issues up to 2022 co-published with and available at:Taylor & Francis
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Journal of Information and Optimization Sciences
Open Access Research Article

Prime video insightful recommender : Unraveling patterns with  TF-IDF and cosine similarity

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pp. 793–802Vol. 46Issue 3April 2025DOI: 10.47974/JIOS-1798XML
Received:
11 Sep 2024
Published Online:
05 Apr 2025
Article type:
Research Article
Language:
EN
Article no.:
JIOS-1798
Pages:
793–802

Abstract

Amazon Prime Video, a prominent on-demand broadcast platform, has achieved a remarkable global reach, extending its services to more than 200 countries and territories. This study delves into the dynamic landscape of Amazon Prime Video, utilizing a dataset of 9,668 records obtained from Kaggle. Our approach involves an exploratory analysis to distill essential insights into the available content on this prominent on-demand broadcast platform. In tandem, we implemented the recommendations using well-designed TF-IDF and cosine similarity algorithms in natural language processing (NLP).Our exploration unveils intriguing data on current content trends. Despite current limitations in the recommendation system, its potential shines through when considering the integration of additional features. This research endeavors to contribute nuanced perspectives on Amazon Prime Video’s content landscape, offering insights into emerging trends and the promise held by recommendation algorithms within the platform’s dynamic ecosystem.

Keywords

Prime videoEDARecommendationNLPTF-IDFCosinesimilarity

Subject Classifications

68T0968T5068W27

References

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