TL;DR Develop stochastic gradient descent algorithm in Python from scratch. Create a ratings matrix from last.fm dataset and preprocess the data. Train the model using SGD and use it to recommend music artists.
Recommender system is everywhere in our lives today, it has played a big role in our lives today in helping us to make decisions, so in this repo we will look at one of the way to recommender artist to our users
- Content Based -> Based on properties of items and recommends similar property items
- Collaborative filtering -> Recommends based on knowledge of user preferences on items
This recommender system uses Model-Based Collaborative Filtering which are based on matrix factorization. Goal of matrix factorization is to learn hidden user preferences and item attributes based on exisiting ratings.
X = USV^T
Given m x n matrix X:
- U is (m * r) orthogonal matrix
- S is (r * r) diagonal matrix with non-negative real numbers on the diagonal
- V^T is (r * n) orthogonal matrix
where U represents feature vectors of the users, V represents feature vectors of the items and the elements on the diagonal of S are known as singular values.
View the Jupyter Notebook with descriptions and results
SGD from scratch here
- Make sure you have all the libs installed (pandas, numpy, scikit-learn, seaborn, matplotlib)
- Clone the repo
- Open the
Artist_Recommender.ipynbfile - Restart and run all kernel