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Review Polarity-Wise Recommender.

Han Liu, Yangyang Guo, Jianhua Yin

    IEEE Transactions on Neural Networks and Learning Systems
    |April 15, 2022
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    Summary
    This summary is machine-generated.

    This study introduces a novel review polarity-wise recommender model (RPR) that separately analyzes positive and negative reviews to improve user preference modeling in recommendation systems.

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    Area of Science:

    • Recommender Systems
    • Natural Language Processing
    • Machine Learning

    Background:

    • Review-involved recommender systems enhance recommendations using textual review data.
    • Existing methods often fail to account for opposing aspect information in positive and negative reviews.
    • This can lead to inaccurate user preference modeling and flawed recommendations.

    Purpose of the Study:

    • To develop a recommender system that discriminately processes reviews based on their polarity (positive/negative).
    • To accurately model user-preferred and user-rejected aspects by treating different review types separately.
    • To address the imbalance in semantically different reviews through an aspect-aware importance weighting strategy.

    Main Methods:

    • Proposed a review polarity-wise recommender model (RPR).
    • Separately processed positive and negative reviews to model user-preferred and user-rejected aspects.
    • Implemented an aspect-aware importance weighting strategy to balance review polarity impact.

    Main Results:

    • RPR demonstrated superior performance compared to state-of-the-art baselines across eight benchmark datasets.
    • The model effectively handles opposing aspect information present in reviews of different polarities.
    • Experimental results validate the model's effectiveness in enhancing recommendation accuracy.

    Conclusions:

    • The review polarity-wise recommender model (RPR) offers a significant advancement in review-involved recommendation systems.
    • Discriminately treating review polarities leads to more accurate user preference modeling.
    • The proposed method provides explainable insights for real-world rating prediction applications.