Table of Contents
What is the main objective of matrix factorization in designing recommender systems?
The idea behind matrix factorization is to represent users and items in a lower dimensional latent space.
What is the most common algorithm used to minimize the objective function of matrix factorization?
Common algorithms to minimize the objective function include: Stochastic gradient descent (SGD) is a generic method to minimize loss functions. Weighted Alternating Least Squares (WALS) is specialized to this particular objective.
How does ALS algorithm work?
The alternating least squares (ALS) algorithm factorizes a given matrix R into two factors U and V such that R≈UTV. Since matrix factorization can be used in the context of recommendation, the matrices U and V can be called user and item matrix, respectively.
Is ALS matrix factorization?
Alternating Least Square (ALS) is also a matrix factorization algorithm and it runs itself in a parallel fashion.
How does matrix factorization algorithm work?
Matrix factorization algorithms work by decomposing the original matrix into two matrices one is the upper triangle ( U ), and the other is the lower triangle ( L ). In this tutorial, I would stick with the factorizing the square matrix A into LU, as demonstrated below.
What are the best hyper-parameters for tuning ALS?
regParam: the regularization parameter in ALS (defaults to 1.0) Hyper-parameter tuning is a highly recurring task in many machine learning projects. We can code it up in a function to speed up the tuning iterations. After tuning, we found the best choice of hyper-parameters: maxIter=10, regParam=0.05, rank=20
What is alternating alternating least square (ALS)?
Alternating Least Square (ALS) is also a matrix factorization algorithm and it runs itself in a parallel fashion. ALS is implemented in Apache Spark ML and built for a larges-scale collaborative filtering problems.
How does the WALS algorithm work?
WALS works by initializing the embeddings randomly, then alternating between: Each stage can be solved exactly (via solution of a linear system) and can be distributed. This technique is guaranteed to converge because each step is guaranteed to decrease the loss.
https://www.youtube.com/watch?v=5R1xOJOFRzs