Meta-learning for Matrix Factorization without Shared Rows or Columns
This addresses the challenge of applying matrix factorization to new datasets with different structures, though it is incremental as it builds on existing meta-learning and matrix factorization techniques.
The authors tackled the problem of matrix factorization for unseen matrices without shared rows or columns by meta-learning a neural network that generates prior distributions for factorized matrices, achieving imputation of missing values from limited observations in experiments on three user-item rating datasets.
We propose a method that meta-learns a knowledge on matrix factorization from various matrices, and uses the knowledge for factorizing unseen matrices. The proposed method uses a neural network that takes a matrix as input, and generates prior distributions of factorized matrices of the given matrix. The neural network is meta-learned such that the expected imputation error is minimized when the factorized matrices are adapted to each matrix by a maximum a posteriori (MAP) estimation. We use a gradient descent method for the MAP estimation, which enables us to backpropagate the expected imputation error through the gradient descent steps for updating neural network parameters since each gradient descent step is written in a closed form and is differentiable. The proposed method can meta-learn from matrices even when their rows and columns are not shared, and their sizes are different from each other. In our experiments with three user-item rating datasets, we demonstrate that our proposed method can impute the missing values from a limited number of observations in unseen matrices after being trained with different matrices.