Weighted Alternating Least Squares
An algorithm for minimizing the objective function during matrix factorization in recommendation systems, which allows a downweighting of the missing examples.
Plain English Explanation
An algorithm for minimizing the objective function during matrix factorization in recommendation systems, which allows a downweighting of the missing examples. WALS minimizes the weighted squared error between the original matrix and the reconstruction by alternating between fixing the row factorization and column factorization. Each of these optimizations can be solved by least squares convex optimization. For details, see the Recommendation Systems course.
How is it used?
Practitioners refer to weighted alternating least squares when building, training, or evaluating machine learning systems. It appears in research papers, product documentation, and technical discussions about AI capabilities and limitations.