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A BERT-Based Multi-Criteria Recommender System for Hotel Promotion Management

Author

Listed:
  • Yuanyuan Zhuang

    (School of Management & Department of Big Data Analytics, KyungHee University, 26, Kyungheedae-ro, Dongdaemun-gu, Seoul 02447, Korea)

  • Jaekyeong Kim

    (School of Management & Department of Big Data Analytics, KyungHee University, 26, Kyungheedae-ro, Dongdaemun-gu, Seoul 02447, Korea)

Abstract

Numerous reviews are posted every day on travel information sharing platforms and sites. Hotels want to develop a customer recommender system to quickly and effectively identify potential target customers. TripAdvisor, the travel website that provided the data used in this study, allows customers to rate the hotel based on six criteria: Value, Service, Location, Room, Cleanliness, and Sleep Quality. Existing studies classify reviews into positive, negative, and neutral by extracting sentiment terms through simple sentimental analysis. However, this method has limitations in that it does not consider various aspects of hotels well. Therefore, this study performs fine-tuning the BERT (Bidirectional Encoder Representations from Transformers) model using review data with rating labels on the TripAdvisor site. This study suggests a multi-criteria recommender system to recommend a suitable target customers for the hotel. As the rating values of six criteria of TripAdvisor are insufficient, the proposed recommender system uses fine-tuned BERT to predict six criteria ratings. Based on this predicted ratings, a multi-criteria recommender system recommends personalized Top-N customers for each hotel. The performance of the multi-criteria recommender system suggested in this study is better than that of the benchmark system, a single-criteria recommender system using overall ratings.

Suggested Citation

  • Yuanyuan Zhuang & Jaekyeong Kim, 2021. "A BERT-Based Multi-Criteria Recommender System for Hotel Promotion Management," Sustainability, MDPI, vol. 13(14), pages 1-18, July.
    • Handle: RePEc:gam:jsusta:v:13:y:2021:i:14:p:8039-:d:596842
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    References listed on IDEAS

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    1. Jaekyeong Kim & Ilyoung Choi & Qinglong Li, 2021. "Customer Satisfaction of Recommender System: Examining Accuracy and Diversity in Several Types of Recommendation Approaches," Sustainability, MDPI, vol. 13(11), pages 1-20, May.
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    Cited by:

    1. Arodh Lal Karn & Rakshha Kumari Karna & Bhavana Raj Kondamudi & Girish Bagale & Denis A. Pustokhin & Irina V. Pustokhina & Sudhakar Sengan, 2023. "RETRACTED ARTICLE: Customer centric hybrid recommendation system for E-Commerce applications by integrating hybrid sentiment analysis," Electronic Commerce Research, Springer, vol. 23(1), pages 279-314, March.
    2. Farah Tawfiq Abdul Hussien & Abdul Monem S. Rahma & Hala B. Abdulwahab, 2021. "An E-Commerce Recommendation System Based on Dynamic Analysis of Customer Behavior," Sustainability, MDPI, vol. 13(19), pages 1-21, September.

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