Model Hyperparameter Selection
Simple Baseline Model Epoch vs. Mean Absolute Error
Scalar Pattern Model Epoch vs. Mean Absolute Error
Recommendation Systems via Matrix Factorization
December 2020
This project was the third project for my machine learning class. The project aims to train a machine learning algorithm using MovieLens 100k dataset for movie recommendation by optimizing the model's predictive power. We were given a clean preprocessed version of the MovieLens 100k dataset with 943 users' ratings of 1682 movies. The input to our prediction system is a (user id, movie id) pair. Our predictor's output will be a scalar rating y in range (1,5) — a rating of 1 is the worst possible, a rating of 5 is the best. Our main task is to predict the ratings of all user-movie pairs. The recommendation system is performed using four different models.
- Simple Baseline Model with SGD and Autograd
- One-Scalar-Per-Item Baseline with SGD and Autograd
- One-Vector-Per-Item Collaborative Filtering with SGD and Autograd
- KNNWithMeans classifier from the surprise package