This project provides a recommender system using Collaborative Filtering via Cosine Similarity

The goals of this project are:

• Test various Machine Learning approaches for suitability for use as a recommender
• Implement user derived Collaboritive Filtering to predict users’ ratings on movies using all users.
• Implement item derived Collaboritive Filtering to predict users’ ratings on movies using all items.
• Predict the k most similar movies and users according to each movie and user.
• Improve the user derived approach using only top-K most similar user ratings.
• Prove that Collaboritive Filtering is a simple but effectice means of for recommending in a memory-based context.
• Show that using top-K users vs overall is more accurate.

$ python3 recommender.py

And

$ python3 proj.py

recommender.py written with:

• python3 - v3.7
• scikit-learn - v0.21.3
• matplotlib - v3.1.1
• numpy - v1.17.0
• pandas - v0.25.0

proj.py written with:

• python3 - v3.7
• scikit-learn - v0.15.2
• numpy - v1.17.0
• pandas - v0.15.2
• SciPy - 0.14.0

• Joel Lawrence - joel295
• Deepansh Singh - deepanshsingh8

This project is licensed under the GPL-3.0 License - see the LICENSE.md file for details