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Fast Explainable Recommendation Model by Combining Fine-Grained Sentiment in Review Data.

Ying Wang^1,2, Xin He^2,3, Hongji Wang^2,3

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

Computational Intelligence and Neuroscience

|October 28, 2022

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.