Analysis of Travellers’ Online Reviews in Social Networking Sites Using Fuzzy Logic Approach

  • Mehrbakhsh NilashiEmail author
  • Elaheh Yadegaridehkordi
  • Othman Ibrahim
  • Sarminah Samad
  • Ali Ahani
  • Louis Sanzogni


Social media and digital technology have had significant contributions and impacts on the hospitality and accommodation businesses. Online traveller reviews have been rich sources of information for the traveller’s decision-making process in social media websites. TripAdvisor, a popular travel review site and social media platform, is mainly developed as a free business consultation service to help the travellers to make right decisions in their trips. The aim of this research is to use the multi-criteria ratings provided by the travellers in social media networking sites for developing a new recommender system for hotel recommendations in e-tourism platforms. We extend the crisp-based multi-criteria algorithms to fuzzy-based multi-criteria algorithms for finding the similarities between the travellers based on their provided ratings. To develop the recommendation method, we use clustering and prediction machine learning techniques. We evaluate the recommendation system on TripAdvisor data. Our experiments confirm that the use of clustering and prediction machine learning with the aid of fuzzy-based recommendation algorithms can significantly improve the quality of recommendations in tourism domain.


Fuzzy logic Social networking site Multi-criteria CF TripAdvisor Travellers’ review Health tourism recommender systems 


  1. 1.
    Adomavicius, G., Kwon, Y.: New recommendation techniques for multicriteria rating systems. IEEE Intell. Syst. 22(3), 48–55 (2007)CrossRefGoogle Scholar
  2. 2.
    Berk, R.A.: Classification and Regression Trees (CART) Statistical Learning from a Regression Perspective, pp. 129–186. Springer International Publishing, Cham (2016)Google Scholar
  3. 3.
    Breiman, L., Friedman, J., Stone, C.J., Olshen, R.A.: Classification and Regression Trees. CRC Press, Boca Raton (1984)zbMATHGoogle Scholar
  4. 4.
    Cornelis, C., Lu, J., Guo, X., Zhang, G.: One-and-only item recommendation with fuzzy logic techniques. Inf. Sci. 177(22), 4906–4921 (2007)CrossRefzbMATHGoogle Scholar
  5. 5.
    Deconinck, E., Hancock, T., Coomans, D., Massart, D., Vander Heyden, Y.: Classification of drugs in absorption classes using the classification and regression trees (CART) methodology. J. Pharm. Biomed. Anal. 39(1), 91–103 (2005)CrossRefGoogle Scholar
  6. 6.
    Erensal, Y.C., Öncan, T., Demircan, M.L.: Determining key capabilities in technology management using fuzzy analytic hierarchy process: a case study of Turkey. Inf. Sci. 176(18), 2755–2770 (2006)CrossRefGoogle Scholar
  7. 7.
    Garcia Esparza, S., O’Mahony, M. P., Smyth, B.: A multi-criteria evaluation of a user generated content based recommender system. In: Presented at the 3rd Workshop on Recommender Systems and the Social Web (RSWEB-11), 5th ACM Conference on Recommender Systems, Chicago, IL, USA, 23–27 October 2011 (2011)Google Scholar
  8. 8.
    Ghavipour, M., Meybodi, M.R.: An adaptive fuzzy recommender system based on learning automata. Electron. Commer. Res. Appl. 20, 105–115 (2016)CrossRefGoogle Scholar
  9. 9.
    GüNeri, A.F., Ertay, T., YüCel, A.: An approach based on ANFIS input selection and modeling for supplier selection problem. Expert Syst. Appl. 38(12), 14907–14917 (2011)CrossRefGoogle Scholar
  10. 10.
    Interdonato, R., Romeo, S., Tagarelli, A., Karypis, G.: A versatile graph-based approach to package recommendation. In: 2013 IEEE 25th International Conference on Tools with Artificial Intelligence (ICTAI), pp. 857–864. IEEE (2013)Google Scholar
  11. 11.
    Jang, J.S.: ANFIS: adaptive-network-based fuzzy inference system. IEEE transactions on systems, man, and cybernetics 23(3), 665–685 (1993)CrossRefGoogle Scholar
  12. 12.
    Jannach, D., Gedikli, F., Karakaya, Z., Juwig, O.: Recommending Hotels Based on Multi-dimensional Customer Ratings Information and Communication Technologies in Tourism 2012, pp. 320–331. Springer, Vienna (2012)CrossRefGoogle Scholar
  13. 13.
    Jannach, D., Gedikli, F., Karakaya, Z., Juwig, O.: Recommending Hotels Based on Multi-dimensional Customer Ratings Information and Communication Technologies in Tourism 2012, pp. 320–331. Springer, Vienna (2012)CrossRefGoogle Scholar
  14. 14.
    Jannach, D., Karakaya, Z., & Gedikli, F.: Accuracy improvements for multi-criteria recommender systems. Paper presented at the Proceedings of the 13th ACM conference on electronic commerce (2012)Google Scholar
  15. 15.
    Jannach, D., Karakaya, Z., Gedikli, F.: Accuracy improvements for multi-criteria recommender systems. Paper presented at the Proceedings of the 13th ACM conference on electronic commerce (2012)Google Scholar
  16. 16.
    Jannach, D., Zanker, M., Fuchs, M.: Leveraging multi-criteria customer feedback for satisfaction analysis and improved recommendations. Inf. Technol. Tour. 14(2), 119–149 (2014)CrossRefGoogle Scholar
  17. 17.
    Kermany, N.R., Alizadeh, S.H.: A hybrid multi-criteria recommender system using ontology and neuro-fuzzy techniques. Electron. Commer. Res. Appl. 21, 50–64 (2017)CrossRefGoogle Scholar
  18. 18.
    Kohonen, T., Oja, E., Simula, O., Visa, A., Kangas, J.: Engineering applications of the self-organizing map. Proc. IEEE 84(10), 1358–1384 (1996)CrossRefGoogle Scholar
  19. 19.
    Koren, Y.: Factor in the neighbors: scalable and accurate collaborative filtering. ACM Trans. Knowl. Discov. Data (TKDD) 4(1), 1 (2010)MathSciNetCrossRefGoogle Scholar
  20. 20.
    Kuncheva, L.I.: Classifier ensembles for changing environments. In: International Workshop on Multiple Classifier Systems, pp. 1–15. Springer, Berlin, Heidelberg (2004)Google Scholar
  21. 21.
    Lee, P.J., Hu, Y.H., Lu, K.T.: Assessing the helpfulness of online hotel reviews: a classification-based approach. Telemat. Inform. 35(2), 436–445 (2018)CrossRefGoogle Scholar
  22. 22.
    Li, H., Ye, Q., Law, R.: Determinants of customer satisfaction in the hotel industry: an application of online review analysis. Asia Pac. J. Tour. Res. 18(7), 784–802 (2013)CrossRefGoogle Scholar
  23. 23.
    Li, Q., Wang, C., Geng, G.: Improving personalized services in mobile commerce by a novel multicriteria rating approach. In: Proceedings of the 17th International Conference on World Wide Web, pp. 1235–1236. ACM (2008)Google Scholar
  24. 24.
    Litvin, S.W., Goldsmith, R.E., Pan, B.: Electronic word-of-mouth in hospitality and tourism management. Tour. Manag. 29(3), 458–468 (2008)CrossRefGoogle Scholar
  25. 25.
    Litvin, S.W., Goldsmith, R.E., Pan, B.: A retrospective view of electronic word-of-mouth in hospitality and tourism management. Int. J. Contemp. Hosp. Manag. 30(1), 313–325 (2018)CrossRefGoogle Scholar
  26. 26.
    Liu, L., Mehandjiev, N., Xu, D.-L.: Multi-criteria service recommendation based on user criteria preferences. In: Proceedings of the Fifth ACM Conference on Recommender Systems, pp 77–84. ACM (2011)Google Scholar
  27. 27.
    Martinez-Cruz, C., Porcel, C., Bernabé-Moreno, J., Herrera-Viedma, E.: A model to represent users trust in recommender systems using ontologies and fuzzy linguistic modeling. Inf. Sci. 311, 102–118 (2015)CrossRefGoogle Scholar
  28. 28.
    Nilashi, M., Bagherifard, K., Rahmani, M., Rafe, V.: A recommender system for tourism industry using cluster ensemble and prediction machine learning techniques. Comput. Ind. Eng. 109, 357–368 (2017)CrossRefGoogle Scholar
  29. 29.
    Nilashi, M., Bin Ibrahim, O., Ithnin, N.: Hybrid recommendation approaches for multi-criteria collaborative filtering. Expert Syst. Appl. 41(8), 3879–3900 (2014)CrossRefGoogle Scholar
  30. 30.
    Nilashi, M., Bin Ibrahim, O., Ithnin, N., Sarmin, N.H.: A multi-criteria collaborative filtering recommender system for the tourism domain using expectation maximization (EM) and PCA–ANFIS. Electron. Commer. Res. Appl. 14(6), 542–562 (2015)CrossRefGoogle Scholar
  31. 31.
    Nilashi, M., Ibrahim, O., Yadegaridehkordi, E., Samad, S., Akbari, E., Alizadeh, A.: Travelers decision making using online review in social network sites: a case on tripadvisor. J. Comput. Sci. 28, 168–179 (2018)CrossRefGoogle Scholar
  32. 32.
    O’Connor, P. User-generated content and travel: a case study on Tripadvisor. com. In: Information and communication technologies in tourism 2008, pp. 47–58. Springer (2008)Google Scholar
  33. 33.
    Revinate (2017). Global hotel reputation benchmark report 2017. From Accessed 30 May 2018
  34. 34.
    Shambour, Q., Hourani, M., Fraihat, S.: An item-based multi-criteria collaborative filtering algorithm for personalized recommender systems. Int. J. Adv. Comput. Sci. Appl. 7(8), 274–279 (2016)Google Scholar
  35. 35.
    Soto, J., Melin, P., Castillo, O.: A new approach for time series prediction using ensembles of ANFIS models with interval type-2 and type-1 fuzzy integrators. In: 2013 IEEE Conference on Computational Intelligence for Financial Engineering & Economics (CIFEr), pp. 68–73. IEEE (2013)Google Scholar
  36. 36.
    Sun, J., Li, H.: Listed companies’ financial distress prediction based on weighted majority voting combination of multiple classifiers. Expert Syst. Appl. 35(3), 818–827 (2008)CrossRefGoogle Scholar
  37. 37.
    Thong, N.T.: HIFCF: an effective hybrid model between picture fuzzy clustering and intuitionistic fuzzy recommender systems for medical diagnosis. Expert Syst. Appl. 42(7), 3682–3701 (2015)CrossRefGoogle Scholar
  38. 38.
    Tsai, C.F., Hung, C.: Cluster ensembles in collaborative filtering recommendation. Appl. Soft Comput. 12(4), 1417–1425 (2012)CrossRefGoogle Scholar
  39. 39.
    Vermeulen, I.E., Seegers, D.: Tried and tested: the impact of online hotel reviews on consumer consideration. Tour. Manag. 30(1), 123–127 (2009)CrossRefGoogle Scholar
  40. 40.
    Woods, K., Kegelmeyer, W.P., Bowyer, K.: Combination of multiple classifiers using local accuracy estimates. IEEE Trans. Pattern Anal Mach intell. 19(4), 405–410 (1997)CrossRefGoogle Scholar
  41. 41.
    Yen, C.L.A., Tang, C.H.H.: The effects of hotel attribute performance on electronic word-of-mouth (eWOM) behaviors. Int. J. Hosp. Manag. 76, 9–18 (2019)CrossRefGoogle Scholar
  42. 42.
    Yera, R., Castro, J., Martínez, L.: A fuzzy model for managing natural noise in recommender systems. Appl. Soft Comput. 40, 187–198 (2016)CrossRefGoogle Scholar
  43. 43.
    Yuen, K.K.F.: The fuzzy cognitive pairwise comparisons for ranking and grade clustering to build a recommender system: an application of smartphone recommendation. Eng. Appl. Artif. Intell. 61, 136–151 (2017)CrossRefGoogle Scholar
  44. 44.
    Zadeh, L.A.: Fuzzy sets. Inf. Control 8(3), 338–353 (1965)CrossRefzbMATHGoogle Scholar
  45. 45.
    Zhang, Z., Lin, H., Liu, K., Wu, D., Zhang, G., Lu, J.: A hybrid fuzzy-based personalized recommender system for telecom products/services. Inf. Sci. 235, 117–129 (2013)CrossRefGoogle Scholar

Copyright information

© Taiwan Fuzzy Systems Association 2019

Authors and Affiliations

  • Mehrbakhsh Nilashi
    • 1
    • 2
    Email author
  • Elaheh Yadegaridehkordi
    • 4
  • Othman Ibrahim
    • 5
  • Sarminah Samad
    • 6
  • Ali Ahani
    • 3
    • 7
    • 8
  • Louis Sanzogni
    • 7
  1. 1.Faculty of Information TechnologyTon Duc Thang UniversityHo Chi Minh CityVietnam
  2. 2.Halal Research Center of IRI, FDATehranIran
  3. 3.School of Computing, Faculty of EngineeringUniversiti Teknologi Malaysia (UTM)SkudaiMalaysia
  4. 4.Department of Information Systems, Faculty of Computer Science and Information TechnologyUniversity of MalayaKuala LumpurMalaysia
  5. 5.Azman Hashim International Business SchoolUniversiti Teknologi Malaysia (UTM)SkudaiMalaysia
  6. 6.Department of Business Administration, Collage of Business and AdministrationPrincess Nourah Bint Abdulrahman UniversityRiyadhSaudi Arabia
  7. 7.Department of Business Strategy and Innovation, Griffith Business SchoolGriffith UniversityBrisbaneAustralia
  8. 8.Department of Marketing, Griffith Business SchoolGriffith UniversityGold CoastAustralia

Personalised recommendations