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AspeRa: Aspect-Based Rating Prediction Model

  • Sergey I. Nikolenko
  • Elena Tutubalina
  • Valentin Malykh
  • Ilya Shenbin
  • Anton AlekseevEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11438)

Abstract

We propose a novel end-to-end Aspect-based Rating Prediction model (AspeRa) that estimates user rating based on review texts for the items and at the same time discovers coherent aspects of reviews that can be used to explain predictions or profile users. The AspeRa model uses max-margin losses for joint item and user embedding learning and a dual-headed architecture; it significantly outperforms recently proposed state-of-the-art models such as DeepCoNN, HFT, NARRE, and TransRev on two real world data sets of user reviews. With qualitative examination of the aspects and quantitative evaluation of rating prediction models based on these aspects, we show how aspect embeddings can be used in a recommender system.

Keywords

Aspect-based sentiment analysis Recommender systems Aspect-based recommendation Explainable recommendation User reviews Neural network Deep learning 

Notes

Acknowledgements

This research was done at the Samsung-PDMI Joint AI Center at PDMI RAS and was supported by Samsung Research.

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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Sergey I. Nikolenko
    • 1
    • 4
  • Elena Tutubalina
    • 1
    • 2
    • 4
  • Valentin Malykh
    • 1
    • 3
  • Ilya Shenbin
    • 1
  • Anton Alekseev
    • 1
    Email author
  1. 1.Samsung-PDMI Joint AI CenterSteklov Mathematical Institute at St. PetersburgSaint PetersburgRussia
  2. 2.Chemoinformatics and Molecular Modeling LaboratoryKazan Federal UniversityKazanRussia
  3. 3.Neural Systems and Deep Learning LaboratoryMoscow Institute of Physics and TechnologyDolgoprudnyRussia
  4. 4.Neuromation OUTallinnEstonia

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