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Journal Articles Pattern Recognition Letters Year : 2021

DeepLTRS: A deep latent recommender system based on user ratings and reviews

Abstract

We introduce a deep latent recommender system named deepLTRS in order to provide users with high quality recommendations based on observed user ratings \textit{and} texts of product reviews. The underlying motivation is that, when a user scores only a few products, the texts used in the reviews represent a significant source of information, thereby enhancing the predictive ability of the model. Our approach adopts a variational auto-encoder (VAE) architecture as a deep generative latent model for an ordinal matrix encoding ratings and a document-term matrix encoding the reviews. Taking into account both matrices as model inputs, deepLTRS uses a neural network to capture the relationship between latent factors and latent topics. Moreover, a user-majoring encoder and a product-majoring encoder are constructed to jointly capture user and product preferences. Due to the specificity of the model structure, an original row-column alternated mini-batch optimization algorithm is proposed to deal with user-product dependencies and computational burden. Numerical experiments on simulated and real-world data sets demonstrate that deepLTRS outperforms the state-of-the-art, in particular in context of extreme data sparsity.
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Dates and versions

hal-03021362 , version 1 (24-11-2020)
hal-03021362 , version 2 (10-11-2021)

Identifiers

Cite

Dingge Liang, Marco Corneli, Charles Bouveyron, Pierre Latouche. DeepLTRS: A deep latent recommender system based on user ratings and reviews. Pattern Recognition Letters, 2021, ⟨10.1016/j.patrec.2021.10.022⟩. ⟨hal-03021362v2⟩
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