$Ae^2I$: A Double Autoencoder for Imputation of Missing Values
This addresses the challenge of missing data imputation in domains like recommender systems, though it appears incremental as it builds on existing autoencoder methods by combining two relationships.
The paper tackles the problem of imputing missing values in tabular data by introducing a double autoencoder (Ae^2I) that simultaneously leverages both row-row and column-column relationships, and it demonstrates that Ae^2I significantly outperforms current state-of-the-art models on the Movielens 1M dataset.
The most common strategy of imputing missing values in a table is to study either the column-column relationship or the row-row relationship of the data table, then use the relationship to impute the missing values based on the non-missing values from other columns of the same row, or from the other rows of the same column. This paper introduces a double autoencoder for imputation ($Ae^2I$) that simultaneously and collaboratively uses both row-row relationship and column-column relationship to impute the missing values. Empirical tests on Movielens 1M dataset demonstrated that $Ae^2I$ outperforms the current state-of-the-art models for recommender systems by a significant margin.