https://github.com/annettaqi/recommendation-system-for-movies-using-ml-methods

Collaborative filtering algorithm to recommend movies to users
https://github.com/annettaqi/recommendation-system-for-movies-using-ml-methods

autoencoder knearest-neighbor-algorithm lstm

Last synced: about 1 month ago
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Collaborative filtering algorithm to recommend movies to users

• Host: GitHub
• URL: https://github.com/annettaqi/recommendation-system-for-movies-using-ml-methods
• Owner: AnnettaQi
• Created: 2024-09-30T17:17:28.000Z (3 months ago)
• Default Branch: main
• Last Pushed: 2024-10-17T15:57:53.000Z (3 months ago)
• Last Synced: 2024-10-20T12:21:16.316Z (3 months ago)
• Topics: autoencoder, knearest-neighbor-algorithm, lstm
• Language: Jupyter Notebook
• Homepage:
• Size: 357 KB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

Awesome Lists containing this project

README

        
Data description:

MovieLens 20M movie ratings. 

20 million ratings and 465,000 tag applications applied to 27,000 movies by 138,000 users.

Includes tag genome data with 12 million relevance scores across 1,100 tags.

Collaborative filtering:

Using other similar user's preference, give recommended movies to users.

This project:

This paper utilizes three machine learning models to construct a recommendation system

in Movie dataset. The advantages and disadvantages are discussed in this paper. I

describe one of common issue in recommendation system: sparsity of data. I present

efficient algorithms to tackle this problem. I also discuss other algorithms to mitigate this

problem in the same application as well. Empirical results demonstrate the performance

of the models in movie ratings prediction.

Keywords: 

Recommendation system, Autoencoder, KNN,