Data Mining: How Does Netflix Know What I Might Like?

Tips & Tricks
< | 2-minute read | Pavel />
Data mining image
You've probably noticed it too. When you're watching Netflix or listening to music on Spotify, the platform suddenly starts recommending new content to you. But how do these recommendations come about? It's all thanks to a mysterious process called data mining.

Data mining, or data extraction, is a sophisticated analytical process focused on extracting significant information from large datasets and user information. Its primary goal is to provide businesses with key tools to achieve growth and gain a competitive advantage. Practically, it is applied in increasing the efficiency of advertising campaigns, customer segmentation, and monitoring their preferences and behavior.

When we look at streaming platforms like Netflix, data mining plays a crucial role. These platforms analyze your behavior and preferences—such as your interest in certain genres of movies and series, their length and cast, or your reactions to recommended content. This information is then used to create personalized content recommendations tailored to your interests and preferences.

The data mining process consists of several key steps: understanding the problem, data preparation and cleaning, data analysis, interpretation of results, and deployment of models into practice. Through continuous learning and updating, these models adapt to changes in your behavior and interests, ensuring that the recommended content is always current and relevant.

This dynamic process allows streaming platforms like Netflix not only to better understand their users but also to respond effectively to their needs and ensure an optimal user experience. Your interactions with content, such as actively watching or ignoring certain types of content, further help fine-tune and improve the personalization of recommendations.

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