GitHub - dominic-sagers/MovieLens-20M-Recommender-System: Using the MovieLens 20 Million review dataset, this project aims to explore different ways to design, evaluate, and explain recommender systems algorithms. Different item-based and user-based recommender systems are showcased as well as a hybrid algorithm using a modified page-rank algorithm.

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Using the MovieLens 20 Million review dataset, this project aims to explore different ways to design, evaluate, and explain recommender systems algorithms. Different item-based and user-based recommender systems are showcased as well as a hybrid algorithm using a modified page-rank algorithm.

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Name		Name	Last commit message	Last commit date
Latest commit History 3 Commits
data/movielens_100k		data/movielens_100k
Counterfactually Evaluating Explanations in RecSys.pdf		Counterfactually Evaluating Explanations in RecSys.pdf
Demo-for-QA.ipynb		Demo-for-QA.ipynb
FINALFINAL.ipynb		FINALFINAL.ipynb
Knn_Recommender_System.ipynb		Knn_Recommender_System.ipynb
README.md		README.md
function_tagbased&genre_alina.ipynb		function_tagbased&genre_alina.ipynb
gnn-movielens-real.ipynb		gnn-movielens-real.ipynb
gnn-movielens2-0.ipynb		gnn-movielens2-0.ipynb
main.ipynb		main.ipynb
movielens-group.ipynb		movielens-group.ipynb
normal-movielens.ipynb		normal-movielens.ipynb
notafunctionyet_tagbased&genre_alina.ipynb		notafunctionyet_tagbased&genre_alina.ipynb
recsys-code-group-rec-final.ipynb		recsys-code-group-rec-final.ipynb
recsys-code-group-rec.ipynb		recsys-code-group-rec.ipynb
recsys-graph-algorithm-dom4.ipynb		recsys-graph-algorithm-dom4.ipynb
tagbased&genre_alina.ipynb		tagbased&genre_alina.ipynb