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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 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.
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Dates and versions

hal-02933326 , version 1 (08-09-2020)

Identifiers

  • HAL Id : hal-02933326 , version 1

Cite

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, Nice / Virtual, France. ⟨hal-02933326⟩
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