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Hotel Review Classification Based on the Text Pretraining Heterogeneous Graph Neural Network Model.

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Summary
This summary is machine-generated.

This study introduces a new model integrating Bidirectional Encoder Representation from Transformers (BERT) and a heterogeneous graph attention network (HGAN) for improved travel recommendation. The model achieves 70% accuracy, outperforming existing methods by better understanding user preferences from reviews.

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Area of Science:

  • Artificial Intelligence
  • Natural Language Processing
  • Recommender Systems

Background:

  • The growing volume of online information necessitates precise product recommendations based on user preferences.
  • Current recommendation systems often rely on basic sentiment analysis of reviews, neglecting deeper user demands and reducing classification effectiveness.
  • Effective utilization of user-generated content, like reviews, is crucial for enhancing recommendation accuracy.

Purpose of the Study:

  • To develop and evaluate a novel model that integrates advanced natural language processing and graph neural networks for improved product recommendation.
  • To address the limitations of traditional sentiment analysis in recommendation tasks by incorporating user preference mining.
  • To enhance the accuracy of travel-related product classification by understanding nuanced user demands from reviews.

Main Methods:

  • Fine-tuning Bidirectional Encoder Representation from Transformers (BERT) on a large dataset of 1.4 million hotel reviews to capture trip-related word representations.
  • Employing a similarity fussy-matching method to identify the main topics within user reviews.
  • Constructing a heterogeneous graph attention network (HGAN) with an attention mechanism to mine user travel preferences.
  • Integrating BERT and HGAN to perform user preference-based classification for travel recommendations.

Main Results:

  • The proposed model, combining BERT and HGAN, achieved an accuracy of 70% in travel type classification.
  • Experimental results demonstrated that the new model significantly outperformed five other baseline models in the classification task.
  • The fine-tuned BERT model effectively generated rich representations of trip-related terms, aiding in preference mining.

Conclusions:

  • The integration of BERT and HGAN offers a superior approach to recommendation systems compared to traditional methods.
  • The model's ability to mine user preferences from reviews enhances the effectiveness of product classification and recommendation.
  • This approach provides a promising direction for developing more sophisticated and accurate recommender systems in e-commerce.