https://github.com/sbwho/movie-recommendation-system

Movie Recomendation System is a movie recommender system using the TMDB 5000 Movie Dataset on Kaggle. Main goal of this system is to develop essential skills in data handling, exploratory data analysis, and model building
https://github.com/sbwho/movie-recommendation-system

content-based-filtering cosine-similarity demographic-filtering idbm-reviews ipynb kaggle machine movie-recommendation python sklearn

Last synced: about 1 month ago
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Movie Recomendation System is a movie recommender system using the TMDB 5000 Movie Dataset on Kaggle. Main goal of this system is to develop essential skills in data handling, exploratory data analysis, and model building

• Host: GitHub
• URL: https://github.com/sbwho/movie-recommendation-system
• Owner: SBwho
• License: mit
• Created: 2023-06-13T01:20:28.000Z (over 1 year ago)
• Default Branch: main
• Last Pushed: 2023-06-13T01:34:32.000Z (over 1 year ago)
• Last Synced: 2024-11-13T08:41:08.854Z (about 1 month ago)
• Topics: content-based-filtering, cosine-similarity, demographic-filtering, idbm-reviews, ipynb, kaggle, machine, movie-recommendation, python, sklearn
• Language: Jupyter Notebook
• Homepage:
• Size: 41 KB
• Stars: 2
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

        
# Movie-Recommendation-System

get the database from- https://www.google.com/search?q=imdb+weighted+rating+formula&oq=imdb+weight&gs_lcrp=EgZjaHJvbWUqBwgAEAAYgAQyBwgAEAAYgAQyBwgBEAAYgAQyBggCEEUYOTIHCAMQABiABDIHCAQQABiABDIICAUQABgWGB4yCAgGEAAYFhgeMgoIBxAAGAoYFhgeMggICBAAGBYYHjIICAkQABgWGB7SAQg5NTAwajBqN6gCALACAA&sourceid=chrome&ie=UTF-8#imgrc=XEZD9hp89-nzJM

# THERE ARE COUPLE OF RECOMMENDATION SYSTEMS-

  # 1.Demographic Filtering -It offer generalized recommendations to every user, based on movie popularity and/or genre.

                        - The System recommends the same movies to users with similar demographic features. 

                        - Since each user is different , this approach is considered to be too simple.

                        - The basic idea behind this system is that movies that are more popular and

                        - critically acclaimed will have a higher probability of being liked by the average audience.

  # 2.Content Based Filtering - It suggest similar items based on a particular item. 

                        - This system uses item metadata, such as genre, director, description, actors, etc. for movies, to make these recommendations. 

                        - The general idea behind these recommender systems is that if a person liked a particular item, 

                        - He or she will also like an item that is similar to it.