Content Based Recommendation System on Netflix Data

Deepti Sharma, Deepshikha Aggarwal, D. A. B. Saxena
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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.
基于 Netflix 数据的内容推荐系统
大流行之后,OTT 平台成为向用户提供娱乐的最常见平台。在所有平台中,Netflix 已成为最受欢迎的平台。Netflix 数据的可视化可以在许多方面提供有价值的见解和益处,如了解观众偏好、内容优化、个性化推荐、质量和内容性能评估、欺诈检测等。这项研究提供了探索性的数据可视化,并在 Netflix 数据上提供了基于内容的推荐系统,因为在现实应用中,公司会使用这些推荐系统算法来确定哪个系统更适合提高用户对平台的参与度。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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