Content Based Recommendation System on Netflix Data

Author:

Sharma Dr. Deepti,Aggarwal Dr. Deepshikha,Saxena Dr. Archana B.

Abstract

After pandemic, OTT platforms are the most common platform to provide entertainment to users. Among all platforms, Netflix has become most the popular one. Data visualization of Netflix data can provide valuable insights and benefits in many ways like understanding viewer preferences, content optimization, personalized recommendation, quality and content performance evaluation, fraud detection to name a few. This research provides exploratory data visualization and provide a content based recommendation system on Netflix data as in real world applications, company uses these recommendation system algorithms to determine which system are better to improve users’ engagement of the platform.

Publisher

HM Publishers

Reference10 articles.

1. Sharma D., Saxena B. A., Aggarwal D. “Exploratory Sentiment Analysis of Sales Data”, “European Economic Letters” (ABDC Journal, C Category), ISSN: 2323-5233, Vol 13 No. 4, October 2023, Page no: 982-986.

2. G. Adomaviciu and A. Tuzhilin, "Toward the next generation of recommender systems: a survey of the state-of-the-art and possible extensions," IEEE, vol. 17, no. 6, pp. 734-749, 2005

3. G. Linden, B. Smith and J. York, "Amazon.com recommendations: item-to-item collaborative filtering," IEEE Internet Computing, vol. 7, no. 1, pp. 76-80, 2003.

4. J. Lu, D. Wu and W. W. G. Z. Mingsong Mao, "Recommender system application developments: A survey," Decision Support Systems, vol. 74, pp. 12-32, 2015.

5. "Movie Dataset: Budgets, Genres, Insights," [Online]. Available: https://www.kaggle.com/datasets/utkarshx27/movies-dataset. [Accessed 3 March 2023].

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