Ae2I: A Double Autoencoder for Imputation of Missing Values
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Abstract (arXiv)
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 (Ae2I) 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 Ae2I outperforms the current state-of-the-art models for recommender systems by a significant margin.
Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 11 of its 37 references that are in the map. First posted on arXiv 16 Jan 2023.
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References · 11
- 2018GAIN: Missing Data Imputation using Generative Adversarial Netscited by 352
- 2018Handling Incomplete Heterogeneous Data using VAEscited by 149
- 2019Behavior Sequence Transformer for E-commerce Recommendation in Alibabacited by 130
- 2019Inductive Matrix Completion Based on Graph Neural Networkscited by 86
- 2018Deep Models of Interactions Across Setscited by 69
- 2017VIGAN: Missing View Imputation with Generative Adversarial Networkscited by 33
- 2015Neural Network Matrix Factorizationcited by 26
- 2016A Neural Autoregressive Approach to Collaborative Filteringcited by 23
- 2016Hybrid Recommender System based on Autoencoderscited by 14
- 2019Joint Neural Collaborative Filtering for Recommender Systemscited by 11
- 2021GLocal-K: Global and Local Kernels for Recommender Systemscited by 11
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