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Netflix AI Recommendations Dataset
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Free
About
Netflix has revolutionised entertainment consumption by offering a vast library of movies, series, documentaries, and original productions to millions of users globally. To enhance transparency and user engagement, interactive visualisation tools have been introduced, providing insights into how Netflix's advanced recommendation system operates. This system is powered by artificial intelligence and machine learning, personalising content suggestions. The data illuminates how these recommendations are generated, enabling more efficient and engaging content discovery. The underlying mechanics include user behaviour analysis, content-based filtering, and collaborative filtering, all bolstered by deep learning models to improve prediction accuracy. The interactive visualisation tools allow users to understand content clustering, view heatmaps for popularity trends, and access personalised recommendation graphs.
Columns
- N_id: A unique numerical identifier for each content entry.
- Title: The name of the movie, series, or documentary. There are 6350 unique titles in the sample data, with "Wild Dog" being a most common entry.
- Main Genre: The primary categorisation of the content. There are 20 unique main genres, with "Drama" and "Comedy" being the most prevalent.
- Sub Genres: More specific classifications for the content. The dataset contains 3257 unique sub genres, with "Stand-Up Comedy" appearing frequently.
- Release Year: The year the content was initially released, ranging from 1962 to 2025.
- Maturity Rating: The age-appropriateness rating of the content, with 5 unique ratings, including "U/A 16+" and "A".
- Original Audio: The initial audio language of the content. There are 288 unique entries, with "N/A" and "English [Original]" being common.
- Recommendations: Information pertaining to the content's recommendations. There are 6393 unique values in this column.
Distribution
The dataset is provided as a data file, typically in CSV format, specifically
Netflix Data new.csv. It is approximately 1.58 MB in size and consists of 8 columns. The sample data consistently shows 6403 valid records across its columns. Specific row or record counts beyond this are not available in the provided details.Usage
This dataset is ideal for applications aimed at understanding and enhancing content discovery and recommendation systems. It can be used for developing and evaluating artificial intelligence and machine learning models, particularly deep learning models, focused on personalisation and prediction accuracy. Data analysts can explore user behaviour patterns, content categorisation, and popularity trends. Furthermore, it is suitable for creating interactive visualisations that provide insights into how content is clustered, how popularity trends evolve, and how individual user interactions influence future recommendations. Researchers can leverage it to study recommendation algorithms, while content strategists might use it to inform content acquisition and placement decisions.
Coverage
The dataset's scope is global, with features like "regional or global popularity trends" indicating broad applicability. The time range for content release years spans from 1962 to 2025, suggesting a wide historical and forward-looking view of content. Demographic scope is inferred through user behaviour analysis, content preferences, and maturity ratings, which cater to different age groups. The dataset is expected to be updated annually.
License
CC0: Public Domain
Who Can Use It
This dataset is valuable for data scientists, machine learning engineers, and AI practitioners who are building or studying recommendation systems. Data analysts and business intelligence professionals can use it to uncover trends and patterns in entertainment consumption. Researchers in fields like human-computer interaction or media studies might find it useful for examining user engagement and content discovery mechanisms. Furthermore, anyone with an interest in understanding the dynamics of streaming platforms and personalised content delivery would benefit from this data.
Dataset Name Suggestions
- Netflix Recommendation System Data
- Interactive Netflix Content Analytics
- Netflix AI Recommendations Dataset
- Streaming Content Personalisation Data
- Netflix Content Discovery Insights
Attributes
Original Data Source: Netflix AI Recommendations Dataset
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