Recommender System

Part 1: SVD&SVD++

• Implemented SVD and SVD++ using Multiplicative update rules
• Used KL divergence and Euclidean distance to compute cost, respectively.

Part 2: Movie Recommender

• Developed a recommender system based on the SVD++ algorithm to predict users’ preferences for unseen movies based on their similarity to other users

• The dataset contains 100,000 ratings from 1000 users on 1700 movies. Data Source: https://grouplens.org/datasets/movielens/100k/