Skip to Main content Skip to Navigation
Conference papers

Missing rating imputation based on product reviews via deep latent variable models

Dingge Liang 1 Marco Corneli 2, 1 Pierre Latouche 2 Charles Bouveyron 3
1 MAASAI - Modèles et algorithmes pour l’intelligence artificielle
CRISAM - Inria Sophia Antipolis - Méditerranée , UNS - Université Nice Sophia Antipolis (... - 2019), JAD - Laboratoire Jean Alexandre Dieudonné, Laboratoire I3S - SPARKS - Scalable and Pervasive softwARe and Knowledge Systems
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 autoen-coder 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.
Complete list of metadata

Cited literature [14 references]  Display  Hide  Download
Contributor : Dingge Liang <>
Submitted on : Tuesday, September 8, 2020 - 1:05:40 PM
Last modification on : Thursday, January 21, 2021 - 2:32:02 PM
Long-term archiving on: : Wednesday, December 2, 2020 - 10:43:03 PM


Files produced by the author(s)


  • HAL Id : hal-02933326, version 1


Dingge Liang, Marco Corneli, Pierre Latouche, Charles Bouveyron. Missing rating imputation based on product reviews via deep latent variable models. ICML2020 Workshop on the Art of Learning with Missing Values (Artemiss), Jul 2020, Virtual, France. ⟨hal-02933326⟩



Record views


Files downloads