IDEAS home Printed from https://ideas.repec.org/a/eee/jomega/v130y2025ics0305048324001385.html
   My bibliography  Save this article

Hotel recommendation mechanism based on online reviews considering multi-attribute cooperative and interactive characteristics

Author

Listed:
  • Zhang, Chonghui
  • Cheng, Xinru
  • Li, Kai
  • Li, Bo

Abstract

Online reviews of hotels provide important information to consumers. The process of extracting useful information from diverse online reviews is crucial for making the best final decisions. To explore the hidden intrinsic information behind online reviews, this paper optimizes information extraction by integrating multiple sources, and gives the recommendation alternative. First, to meet quantitative requirements, the probabilistic linguistic term set is introduced to demonstrate the massive number of comments crawled. Second, considering preference and fluctuation, the relative importance of multiple attributes is determined. Because multiple attributes typically have cooperative or mutually exclusive relationships, a novel model is presented by introducing such relationship to modify relative importance. Third, inspired by the 2-additive Choquet integral operator and the Mahalanobis-Taguchi System, a bi-objective optimization model is proposed to illustrate the interactive effect of comments and develop an attribute correlation network. The specific relationships between attributes are reflected, including the positive and negative interactions. The relative importance, interactive imporantce and subgroup utility can be obtained. Fourth, to guarantee the operability and interpretability of the recommendation results, this paper presents a new information fusion operator and an probabilistic linguistic three-way recommendation process. Finally, a case study is used to demonstrate the complete procedures, and the parameter and comparative analyses highlight the effectiveness of the new operator and recommendation method.

Suggested Citation

  • Zhang, Chonghui & Cheng, Xinru & Li, Kai & Li, Bo, 2025. "Hotel recommendation mechanism based on online reviews considering multi-attribute cooperative and interactive characteristics," Omega, Elsevier, vol. 130(C).
  • Handle: RePEc:eee:jomega:v:130:y:2025:i:c:s0305048324001385
    DOI: 10.1016/j.omega.2024.103173
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0305048324001385
    Download Restriction: Full text for ScienceDirect subscribers only

    File URL: https://libkey.io/10.1016/j.omega.2024.103173?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Wang, Ying-Ming & Chin, Kwai-Sang, 2011. "The use of OWA operator weights for cross-efficiency aggregation," Omega, Elsevier, vol. 39(5), pages 493-503, October.
    2. Liu, Fan & Liao, Huchang & Al-Barakati, Abdullah, 2023. "Physician selection based on user-generated content considering interactive criteria and risk preferences of patients," Omega, Elsevier, vol. 115(C).
    3. Geetha, M. & Singha, Pratap & Sinha, Sumedha, 2017. "Relationship between customer sentiment and online customer ratings for hotels - An empirical analysis," Tourism Management, Elsevier, vol. 61(C), pages 43-54.
    4. Zelin Zhang & Kejia Yang & Jonathan Z. Zhang & Robert W. Palmatier, 2023. "Uncovering Synergy and Dysergy in Consumer Reviews: A Machine Learning Approach," Management Science, INFORMS, vol. 69(4), pages 2339-2360, April.
    5. Wu, Xingli & Liao, Huchang, 2023. "A compensatory value function for modeling risk tolerance and criteria interactions in preference disaggregation," Omega, Elsevier, vol. 117(C).
    6. Zhu, John Jianjun & Chang, Yung-Chun & Ku, Chih-Hao & Li, Stella Yiyan & Chen, Chi-Jen, 2021. "Online critical review classification in response strategy and service provider rating: Algorithms from heuristic processing, sentiment analysis to deep learning," Journal of Business Research, Elsevier, vol. 129(C), pages 860-877.
    7. Mingzhen Zhang & Naiding Yang & Xianglin Zhu & Yan Wang, 2024. "A novel probabilistic linguistic multi-attribute decision-making method based on Mahalanobis–Taguchi system and fuzzy measure," Journal of the Operational Research Society, Taylor & Francis Journals, vol. 75(2), pages 246-261, February.
    8. Xiangqian Feng & Xiaodong Pang & Lan Zhang, 2020. "On consistency and priority weights for interval probabilistic linguistic preference relations," Fuzzy Optimization and Decision Making, Springer, vol. 19(4), pages 529-560, December.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Wang, Mingwei & Zhang, Junping & Liang, Decui, 2024. "Multi-period share pledging with sequential three-way proportion decision," Omega, Elsevier, vol. 127(C).
    2. Wen, Tao & Chen, Yu-wang & Syed, Tahir abbas & Wu, Ting, 2024. "ERIUE: Evidential reasoning-based influential users evaluation in social networks," Omega, Elsevier, vol. 122(C).
    3. Liu, Hui-hui & Song, Yao-yao & Liu, Xiao-xiao & Yang, Guo-liang, 2020. "Aggregating the DEA prospect cross-efficiency with an application to state key laboratories in China," Socio-Economic Planning Sciences, Elsevier, vol. 71(C).
    4. Renata Machado de Andrade & Suhyung Lee & Paul Tae-Woo Lee & Oh Kyoung Kwon & Hye Min Chung, 2019. "Port Efficiency Incorporating Service Measurement Variables by the BiO-MCDEA: Brazilian Case," Sustainability, MDPI, vol. 11(16), pages 1-18, August.
    5. Oral, Muhittin & Oukil, Amar & Malouin, Jean-Louis & Kettani, Ossama, 2014. "The appreciative democratic voice of DEA: A case of faculty academic performance evaluation," Socio-Economic Planning Sciences, Elsevier, vol. 48(1), pages 20-28.
    6. Amal Almansour & Reem Alotaibi & Hajar Alharbi, 2022. "Text-rating review discrepancy (TRRD): an integrative review and implications for research," Future Business Journal, Springer, vol. 8(1), pages 1-15, December.
    7. Yangxue Ning & Yan Zhang & Guoqiang Wang, 2023. "An Improved DEA Prospect Cross-Efficiency Evaluation Method and Its Application in Fund Performance Analysis," Mathematics, MDPI, vol. 11(3), pages 1-15, January.
    8. Doris Chenguang Wu & Shiteng Zhong & Richard T R Qiu & Ji Wu, 2022. "Are customer reviews just reviews? Hotel forecasting using sentiment analysis," Tourism Economics, , vol. 28(3), pages 795-816, May.
    9. Zhang, Chonghui & Zhang, Na & Su, Weihua & Balezentis, Tomas, 2024. "Online commodity recommendation model for interaction between user ratings and intensity-weighted hierarchical sentiment: A case study of LYCOM," Omega, Elsevier, vol. 129(C).
    10. Md Shamim Hossain & Mst Farjana Rahman, 2023. "Customer Sentiment Analysis and Prediction of Insurance Products’ Reviews Using Machine Learning Approaches," FIIB Business Review, , vol. 12(4), pages 386-402, December.
    11. Kao, Chiang & Liu, Shiang-Tai, 2020. "A slacks-based measure model for calculating cross efficiency in data envelopment analysis," Omega, Elsevier, vol. 95(C).
    12. H. Örkcü & Mehmet Ünsal & Hasan Bal, 2015. "A modification of a mixed integer linear programming (MILP) model to avoid the computational complexity," Annals of Operations Research, Springer, vol. 235(1), pages 599-623, December.
    13. Feng Li & Han Wu & Qingyuan Zhu & Liang Liang & Gang Kou, 2021. "Data envelopment analysis cross efficiency evaluation with reciprocal behaviors," Annals of Operations Research, Springer, vol. 302(1), pages 173-210, July.
    14. Nan Yang & Nikolaos Korfiatis & Dimitris Zissis & Konstantina Spanaki, 2024. "Incorporating topic membership in review rating prediction from unstructured data: a gradient boosting approach," Annals of Operations Research, Springer, vol. 339(1), pages 631-662, August.
    15. Azadi, Majid & Yousefi, Saeed & Farzipoor Saen, Reza & Shabanpour, Hadi & Jabeen, Fauzia, 2023. "Forecasting sustainability of healthcare supply chains using deep learning and network data envelopment analysis," Journal of Business Research, Elsevier, vol. 154(C).
    16. Lee Yen Chaw & Chun Meng Tang, 2019. "Online accommodation booking: what information matters the most to users?," Information Technology & Tourism, Springer, vol. 21(3), pages 369-390, September.
    17. Yang, Guo-liang & Yang, Jian-bo & Liu, Wen-bin & Li, Xiao-xuan, 2013. "Cross-efficiency aggregation in DEA models using the evidential-reasoning approach," European Journal of Operational Research, Elsevier, vol. 231(2), pages 393-404.
    18. Ruiz, José L. & Sirvent, Inmaculada, 2012. "On the DEA total weight flexibility and the aggregation in cross-efficiency evaluations," European Journal of Operational Research, Elsevier, vol. 223(3), pages 732-738.
    19. Abhishek Tandon & Aakash Aakash & Anu G. Aggarwal & P. K. Kapur, 0. "Analyzing the impact of review recency on helpfulness through econometric modeling," International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 0, pages 1-8.
    20. Huimin Xiao & Shouwen Wu & Chunsheng Cui, 2022. "The Research on Consistency Checking and Improvement of Probabilistic Linguistic Preference Relation Based on Similarity Measure and Minimum Adjustment Model," Mathematics, MDPI, vol. 10(9), pages 1-18, April.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:jomega:v:130:y:2025:i:c:s0305048324001385. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/wps/find/journaldescription.cws_home/375/description#description .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.