Built a movie recommender system that considers user-user similarity, item-item similarity, global averages, and matrix factorization. This system resulted in 0.67 RMSE, 19.86 MAPE on the unseen test data.
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A movie recommender system that considers user-user similarity, item-item similarity, global averages, and matrix factorization.