https://github.com/harshitaphadtare/moive-recommender-system

A content-based movie recommender system suggests movies to users based on the similarity of movie attributes. It analyzes features like genre, actors, directors, and plot keywords to find movies that match the user's preferences.
https://github.com/harshitaphadtare/moive-recommender-system

data numpy python stemming vectorization

Last synced: about 1 month ago
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A content-based movie recommender system suggests movies to users based on the similarity of movie attributes. It analyzes features like genre, actors, directors, and plot keywords to find movies that match the user's preferences.

• Host: GitHub
• URL: https://github.com/harshitaphadtare/moive-recommender-system
• Owner: harshitaphadtare
• Created: 2023-10-24T08:33:11.000Z (almost 2 years ago)
• Default Branch: main
• Last Pushed: 2023-12-13T20:08:28.000Z (almost 2 years ago)
• Last Synced: 2025-06-09T14:05:18.389Z (4 months ago)
• Topics: data, numpy, python, stemming, vectorization
• Language: Jupyter Notebook
• Homepage:
• Size: 8.7 MB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

          
# movie recommender system

The following steps outline the methodology behind the Movie Recommender System:

Data Collection - Gather comprehensive data on movies, including attributes such as genre, plot summaries, and other relevant features.

Vectorization - Transform movie descriptions into high-dimensional vectors using advanced vectorization techniques. This step ensures a numerical representation of textual data for efficient analysis.

Stemming -  Apply stemming to each word in movie descriptions, reducing them to their root form. This process simplifies the comparison of textual information and improves the system's ability to identify commonalities.