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Movie Recommender System Data
Product Reviews & Feedback
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Free
About
This dataset provides metadata for approximately 10,000 popular movies sourced from The Movie Database (TMDB). It serves as an excellent foundation for developing and experimenting with recommendation systems, a technology prevalent in modern digital platforms such as Netflix and YouTube. The data was meticulously assembled from the official TMDB API during a data science project, aiming to facilitate extensive exploratory data analysis to uncover the history and narrative of cinema, and to construct various types of recommender engines.
Columns
- id: A unique identity document number for each movie.
- original_language: The original language in which the movie was produced.
- original_title: The original title of the movie.
- overview: A short review or summary of the movie's plot or context.
- popularity: An indicator of the movie's fame or current trend on TMDB.
- release_date: The date when the movie was originally released.
- title: The title of the movie.
- vote_average: The average rating given to the movie by voters on TMDB.
- vote_count: The total number of votes received by the movie on TMDB.
Distribution
The dataset is typically provided as a CSV file, named "TMDB 10000 Movies Dataset.csv", and has a size of 3.47 MB. It contains metadata for around 10,000 movies. Most columns feature valid data for all 10,000 records, though a minimal number of entries may be missing for certain fields, such as 'overview'. The content consists of more than just rows and columns; it represents 10,000 popular movies based on TMDB ratings, making it an ideal database for starting with recommendation algorithms.
Usage
This dataset is perfectly suited for several applications and use cases, including:
- Building sophisticated Content-Based and Collaborative Filtering Based Recommendation Engines.
- Predicting movie revenue or overall movie success based on various metrics.
- Analysing trends in movie popularity, such as which films attract higher vote counts and average ratings on TMDB.
- Performing detailed Exploratory Data Analysis (EDA) on movie-related information to explore the evolution and stories within cinema.
- Serving as a starting point for anyone looking to develop or study recommendation systems.
Coverage
The dataset's scope spans a wide time range, with movie release dates from 10 June 1895 to 25 November 2022. While not explicitly global in geographic terms, the presence of 44 unique original languages, with English making up 77% and French 7%, suggests an international collection of movies. It focuses on 10,000 popular movies as determined by TMDB ratings, reflecting a broad audience interest without specific demographic segmentation.
License
CC0: Public Domain
Who Can Use It
This dataset is beneficial for a diverse group of users:
- Data Scientists and Students: Ideal for those undertaking semester projects in data science, providing a practical foundation for working with real-world movie data.
- Machine Learning Engineers: Particularly useful for individuals focused on developing and refining recommendation system algorithms for platforms like Netflix or Amazon Prime.
- Film Historians and Researchers: Can be used for in-depth analysis of cinema trends, popularity, and success metrics over time.
- Developers: Anyone looking for a robust and well-structured database to implement and test recommendation functionalities.
Dataset Name Suggestions
- TMDB 10000 Movies Dataset
- Movie Recommender System Data
- Popular Film Metadata from TMDB
- Cinema Data for Recommendation Engines
- Global Movie Ratings and Overview
Attributes
Original Data Source: Movie Recommender System Data
QUALITY