• English
  • Deutsch
  • Log In
    Password Login
    or
  • Research Outputs
  • Projects
  • Researchers
  • Institutes
  • Statistics
Repository logo
Fraunhofer-Gesellschaft
  1. Home
  2. Fraunhofer-Gesellschaft
  3. Konferenzschrift
  4. Recurrent Point Review Models
 
  • Details
  • Full
Options
28 September 2020
Conference Paper
Titel

Recurrent Point Review Models

Abstract
Deep neural network models represent the state-of-the-art methodologies for natural language processing. Here we build on top of these methodologies to incorporate temporal information and model how review data changes with time. Specifically, we use the dynamic representations of recurrent point process models, which encode the history of how business or service reviews are received in time, to generate instantaneous language models with improved prediction capabilities. Simultaneously, our methodologies enhance the predictive power of our point process models by incorporating summarized review content representations. We provide recurrent network and temporal convolution solutions for modeling the review content. We deploy our methodologies in the context of recommender systems, effectively characterizing the change in preference and taste of users as time evolves. Source code is available at [1].
Author(s)
Cvejoski, Kostadin
Sánchez, Ramsés J.
Georgiev, Bogdan
Bauckhage, Christian
Ojeda, César
Hauptwerk
International Joint Conference on Neural Networks, IJCNN 2020. Conference proceedings
Project(s)
ML2R
Funder
Bundesministerium für Bildung und Forschung -BMBF-
Konferenz
International Joint Conference on Neural Networks (IJCNN) 2020
Thumbnail Image
DOI
10.1109/IJCNN48605.2020.9206768
Language
English
google-scholar
Fraunhofer-Institut für Intelligente Analyse- und Informationssysteme IAIS
  • Cookie settings
  • Imprint
  • Privacy policy
  • Api
  • Send Feedback
© 2022