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  4. Dynamic Review-based Recommenders
 
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March 20, 2022
Conference Paper
Title

Dynamic Review-based Recommenders

Abstract
Just as user preferences change with time, item reviews also reflect those same preference changes. In a nutshell, if one is to sequentially incorporate review content knowledge into recommender systems, one is naturally led to dynamical models of text. In the present work we leverage the known power of reviews to enhance rating predictions in a way that (i) respects the causality of review generation and (ii) includes, in a bidirectional fashion, the ability of ratings to inform language review models and vice-versa, language representations that help predict ratings end-to-end. Moreover, our representations are time-interval aware and thus yield a continuous-time representation of the dynamics. We provide experiments on real-world datasets and show that our methodology is able to outperform several state-of-the-art models.
Author(s)
Cvejoski, Kostadin  
Fraunhofer-Institut für Intelligente Analyse- und Informationssysteme IAIS  
Sánchez, Ramsés J.
Universität Bonn  
Bauckhage, Christian  
Fraunhofer-Institut für Intelligente Analyse- und Informationssysteme IAIS  
Ojeda, César  
Mainwork
Data Science - Analytics and Applications  
Project(s)
ML2R  
Funder
Bundesministerium für Bildung und Forschung -BMBF-
Conference
International Data Science Conference 2021  
DOI
10.1007/978-3-658-36295-9_10
Language
English
Fraunhofer-Institut für Intelligente Analyse- und Informationssysteme IAIS  
Keyword(s)
  • recurrent recommender networks

  • dynamic language model

  • attention for recommendation

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