https://github.com/khaymanii/movie-recommendation-model

This is a model built using Python and Cosine Similarity algorithm
https://github.com/khaymanii/movie-recommendation-model

matplotlib numpy pandas python scikit-learn

Last synced: 20 days ago
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This is a model built using Python and Cosine Similarity algorithm

• Host: GitHub
• URL: https://github.com/khaymanii/movie-recommendation-model
• Owner: khaymanii
• Created: 2023-03-18T14:38:04.000Z (almost 2 years ago)
• Default Branch: main
• Last Pushed: 2024-02-01T08:26:02.000Z (about 1 year ago)
• Last Synced: 2024-11-20T12:40:59.050Z (3 months ago)
• Topics: matplotlib, numpy, pandas, python, scikit-learn
• Language: Jupyter Notebook
• Homepage:
• Size: 5.05 MB
• Stars: 1
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

        
# Movie Recommendation Model

The Movie Recommendation System employs the cosine similarity algorithm as part of its content-based filtering approach. 

This algorithm is utilized to measure the similarity between movies based on their content features, such as genre, director, and actors. 

Here's a brief overview of how the cosine similarity algorithm works within the context of this system:

## Cosine Similiarity

Cosine similarity is a metric used to quantify the similarity between two vectors. In the context of the Movie Recommendation System, each movie is represented as a vector where each dimension corresponds to a specific content feature. 

The cosine similarity between two movie vectors is calculated using the cosine of the angle between them.