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. WALS minimizes the weighted [squared error](squared error.md) between the original matrix and the reconstruction by alternating between fixing the row factorization and column factorization.