Skip to content

Latest commit

 

History

History
21 lines (18 loc) · 882 Bytes

README.md

File metadata and controls

21 lines (18 loc) · 882 Bytes

Movies Recommender System

Recommender systems are one of the most successful and widespread application of machine learning technologies in business. You can find large scale recommender systems in retail, video on demand, or music streaming.

Algorithms implemented and evaluated

  • Content based filtering
  • Collaborative Filtering
    • Memory based collaborative filtering
      • User-Item Filtering
      • Item-Item Filtering
    • Model based collaborative filtering
      • Single Value Decomposition(SVD)
      • SVD++
  • Hybrid Model
    • Content Based + SVD

Files contained in the project

  • movie_recommendation_system.ipynb : python notebook code file
  • movie_recommendation_system.html : html version of python notebook
  • movies.csv : movies data from MovieLens dataset
  • ratings.csv : rating given by user to movie from MovieLens dataset