Missing Rating Imputation Based on Product Reviews via Deep Latent Variable Models
Abstract
We introduce a deep latent recommender system (deepLTRS) for imputing missing ratings based on the observed ratings and product reviews. Our approach extends a standard variational autoencoder architecture associated with deep latent variable models in order to account for both the ordinal entries and the text entered by users to score and review products. DeepLTRS assumes a latent representation of both users and products, allowing a natural visualisation of the positioning of users in relation to products. Numerical experiments on simulated and real-world data sets demonstrate that DeepLTRS outperforms the state-of-the-art, in particular in contexts of extreme data sparsity.
Cite
Text
Liang et al. "Missing Rating Imputation Based on Product Reviews via Deep Latent Variable Models." ICML 2020 Workshops: Artemiss, 2020.Markdown
[Liang et al. "Missing Rating Imputation Based on Product Reviews via Deep Latent Variable Models." ICML 2020 Workshops: Artemiss, 2020.](https://mlanthology.org/icmlw/2020/liang2020icmlw-missing/)BibTeX
@inproceedings{liang2020icmlw-missing,
title = {{Missing Rating Imputation Based on Product Reviews via Deep Latent Variable Models}},
author = {Liang, Dingge and Corneli, Marco and Latouche, Pierre and Bouveyron, Charles},
booktitle = {ICML 2020 Workshops: Artemiss},
year = {2020},
url = {https://mlanthology.org/icmlw/2020/liang2020icmlw-missing/}
}