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A decision support system for comments-adjusted ranking of hotels

Author

Listed:
  • Xiao-kang Wang
  • Hong-yu Zhang
  • Ya-nan Wang
  • Wen-hui Hou
  • Jian-qiang Wang
  • Hong-gang Peng

Abstract

Increasing online reviews provide a powerful support tool to obtain a relevant hotel selection for tourists. Some reviews seem to have similar contents but their latent meanings are discrepant, due to user preferences. It has a negative effect on the recommendation result without a well processing. To solve this issue, this study develops a cloud-based ranking method to provide hotel recommendation lists for tourists. The method uses the distribution of historical user ratings to adjust original user ratings for minimizing the understanding bias among reviews, and utilizes sentiment analysis and cloud models to quantify text comments. After these, both of them are merged into a holistic score to generate a hotel recommendation list. Our method is validated through a case study of 87,735 users and 835,462 reviews from TripAdvisor.com. Further, our research findings suggest that potential hotel stayers should realize to lower their expectations of a good stay from online reviews, and trust more online reviews of couple and friend travelers.

Suggested Citation

  • Xiao-kang Wang & Hong-yu Zhang & Ya-nan Wang & Wen-hui Hou & Jian-qiang Wang & Hong-gang Peng, 2025. "A decision support system for comments-adjusted ranking of hotels," Journal of the Operational Research Society, Taylor & Francis Journals, vol. 76(9), pages 1880-1897, September.
    • Handle: RePEc:taf:tjorxx:v:76:y:2025:i:9:p:1880-1897
    DOI: 10.1080/01605682.2024.2446683
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