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Exploiting Dual-Attention Networks for Explainable Recommendation in Heterogeneous Information Networks.

Entropy (Basel, Switzerland)ยท2022
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Fast Explainable Recommendation Model by Combining Fine-Grained Sentiment in Review Data.

Ying Wang1,2, Xin He2,3, Hongji Wang2,3

  • 1College of Computer Science and Technology, Jilin University, Changchun 130012, China.

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Summary

This study introduces a novel explainable recommendation system (FSER) that uses fine-grained sentiment analysis from reviews. FSER enhances user experience by providing accurate, efficient, and interpretable recommendations with visual explanations.

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Area of Science:

  • Artificial Intelligence
  • Machine Learning
  • E-commerce Technology

Background:

  • Recommendation systems are crucial in e-commerce for user decision-making and experience.
  • Explainable recommendation systems are gaining importance due to the limitations of implicit feature explanations in traditional matrix factorization.
  • Existing models often struggle with accuracy, sparseness, and training efficiency.

Purpose of the Study:

  • To propose a novel explainable recommendation model, FSER (Fast Fine-grained Sentiment for Explainable Recommendation).
  • To enhance recommendation accuracy, training efficiency, and interpretability.
  • To leverage fine-grained sentiment analysis from review data for improved recommendations.

Main Methods:

  • Constructed three matrices: user-rating, user-aspect sentiment, and item aspect-descriptive word frequency from review data.
  • Reconstructed these matrices using matrix factorization.
  • Utilized reconstructed user-aspect sentiment and item aspect-descriptive word frequency matrices for generating explanations.

Main Results:

  • The FSER model achieved optimal recommendation accuracy compared to classical models.
  • Demonstrated lower sparseness and higher training efficiency than tensor and neural network models.
  • Successfully generated high-quality explanatory texts and diagrams for recommendations.

Conclusions:

  • FSER offers a significant advancement in explainable recommendation systems.
  • The model effectively combines sentiment analysis with matrix factorization for interpretable and efficient recommendations.
  • FSER provides a practical solution for enhancing user trust and understanding in e-commerce platforms.