References
- Abdollahpouri, H., Burke, R., & Mobasher, B. (2017, August 27-31). Controlling popularity bias in learning to rank recommendation. Proceedings of the 11th conference ACM on recommender systems (pp. 42–46).
- Al-Ghossein, M., Abdessalem, T., & Barré, A. (2018). Open data in the hotel industry: Leveraging forthcoming events for hotel recommendation. Information Technology & Tourism, 20(1), 191–216. https://doi.org/https://doi.org/10.1007/s40558-018-0119-6
- An, J., Zhao, S., Lu, X., & Liu, N. (2018). A two-stage multiple-factor aware method for travel product recommendation. Multimedia Tools and Applications, 77(21), 28991–29012. https://doi.org/https://doi.org/10.1007/s11042-018-5992-7
- Camacho, L., & Alves-Souza, S. N. (2018). Social network data to alleviate cold-start in recommender system: A systematic review. Information Processing & Management, 54(4), 529–544. https://doi.org/https://doi.org/10.1016/j.ipm.2018.03.004
- Esmaeili, L., Mardani, S., Golpayegani, S. A. H., & Madar, Z. Z. (2020). A novel tourism recommender system in the context of social commerce. Expert Systems with Applications, 149, 113301. https://doi.org/https://doi.org/10.1016/j.eswa.2020.113301
- Guerreiro, J., & Rita, P. (2020). How to predict explicit recommendations in online reviews using text mining and sentiment analysis. Journal of Hospitality and Tourism Management, 43, 269–272. https://doi.org/https://doi.org/10.1016/j.jhtm.2019.07.001
- Hajli, N., Sims, J., Zadeh, A. H., & Richard, M. O. (2017). A social commerce investigation of the role of trust in a social networking site on purchase intentions. Journal of Business Research, 71, 133–141. https://doi.org/https://doi.org/10.1016/j.jbusres.2016.10.004
- Han, Q., Novais, M. A., & Zejnilovic, L. (2021). Toward travel pattern aware tourism region planning: A big data approach. International Journal of Contemporary Hospitality Management, 33(6), 2157–2175. https://doi.org/https://doi.org/10.1108/IJCHM-07-2020-0673
- Hwang, C. S., & Chen, Y. P. (2007, June 26-29). Using trust in collaborative filtering recommendation. 20th international conference on industrial, engineering and other applications of applied intelligent systems (pp. 1052–1060).
- Kim, J., & Fesenmaier, D. R. (2017). Sharing tourism experiences: The post trip experience. Journal of Travel Research, 56(1), 28–40. https://doi.org/https://doi.org/10.1177/0047287515620491
- Kolahkaj, M., Harounabadi, A., Nikravanshalmani, A., & Chinipardaz, R. (2020). A hybrid context-aware approach for e-tourism package recommendation based on asymmetric similarity measurement and sequential pattern mining. Electronic Commerce Research and Applications, 42, 100978. https://doi.org/https://doi.org/10.1016/j.elerap.2020.100978
- Lee, D. H., & Brusilovsky, P. (2017). Improving personalized recommendations using community membership information. Information Processing & Management, 53(5), 1201–1214. https://doi.org/https://doi.org/10.1016/j.ipm.2017.05.005
- Li, Y., Wu, C., & Lai, C. (2013). A social recommender mechanism for e-commerce: Combining similarity, trust, and relationship. Decision Support Systems, 55(3), 740–752. https://doi.org/https://doi.org/10.1016/j.dss.2013.02.009
- Lin, C. L., & Chang, K. C. (2020). Establishing the service evaluation and selection system for emerging culture festival events using the hybrid MCDM technique. Current Issues in Tourism, 23(18), 2240–2272. https://doi.org/https://doi.org/10.1080/13683500.2019.1665628
- Liu, Z., & Park, S. (2015). What makes a useful online review? Implication for travel product websites. Tourism Management, 47, 140–151. https://doi.org/https://doi.org/10.1016/j.tourman.2014.09.020
- Molinillo, S., Anaya-Sánchez, R., & Liébana-Cabanillas, F. (2020). Analyzing the effect of social support and community factors on customer engagement and its impact on loyalty behaviors toward social commerce websites. Computers in Human Behavior, 108, 105980. https://doi.org/https://doi.org/10.1016/j.chb.2019.04.004
- Neto, A. Q., Dimmock, K., Lohmann, G., & Scott, N. (2020). Destination competitiveness: How does travel experience influence choice? Current Issues in Tourism, 23(13), 1673–1687. https://doi.org/https://doi.org/10.1080/13683500.2019.1641070
- Nie, R., Tian, Z., Wang, J., & Chin, K. (2020). Hotel selection driven by online textual reviews: Applying a semantic partitioned sentiment dictionary and evidence theory. International Journal of Hospitality Management, 88, 102495. https://doi.org/https://doi.org/10.1016/j.ijhm.2020.102495
- Nilashi, M., Ibrahim, O., Ithnin, N., & Sarmin, N. H. (2015). A multi-criteria collaborative filtering recommender system for the tourism domain using expectation maximization (EM) and PCA–ANFIS. Electronic Commerce Research and Applications, 14(6), 542–562. https://doi.org/https://doi.org/10.1016/j.elerap.2015.08.004
- Peter, N., Wang, X. S., Li, X., & June, C. (2020). Reviewing experts’ restraint from extremes and its impact on service providers. Journal of Consumer Research, 47, 654–674. https://doi.org/https://doi.org/10.1093/jcr/ucaa037
- Pop, R. A., Săplăcan, Z., Dabija, D. C., & Alt, M. A. (2021). The impact of social media influencers on travel decisions: the role of trust in consumer decision journey. Current Issues in Tourism, 1–21. https://doi.org/https://doi.org/10.1080/13683500.2021.1895729
- Shafieizadeh, K., & Tao, C. W. (2020). How does a menu's information about local food affect restaurant selection? The roles of corporate social responsibility, transparency, and trust. Journal of Hospitality and Tourism Management, 43, 232–240. https://doi.org/https://doi.org/10.1016/j.jhtm.2020.04.007
- Sohrabi, B., Vanani, I. R., Nasiri, N., & Rud, A. G. (2020). A predictive model of tourist destinations based on tourists’ comments and interests using text analytics. Tourism Management Perspectives, 35, 100710. https://doi.org/https://doi.org/10.1016/j.tmp.2020.100710
- Tang, M., & Liao, H. (2019). From conventional group decision-making to large-scale group decision-making: What are the challenges and how to meet them in big data era? A state-of-the-art survey. Omega, 100, 102141. https://doi.org/https://doi.org/10.1016/j.omega.2019.102141
- Wu, J., Chang, J., Cao, Q., & Liang, C. (2019). A trust propagation and collaborative filtering-based method for incomplete information in social network group decision making with type-2 linguistic trust. Computers & Industrial Engineering, 127, 853–864. https://doi.org/https://doi.org/10.1016/j.cie.2018.11.020
- Ye, F., Xia, Q., Zhang, M., Zhan, Y., & Li, Y. (2020). Harvesting online reviews to identify the competitor set in a service business: Evidence from the hotel industry. Journal of Service Research, https://doi.org/https://doi.org/10.1177/1094670520975143
- Zhang, C., Zhang, H., & Wang, J. (2018). Personalized restaurant recommendation method combining group correlations and customer preferences. Information Sciences, 454–455, 128–143. https://doi.org/https://doi.org/10.1016/j.ins.2018.04.061
- Zhang, H., Ji, P., Wang, J., & Chen, X. (2017). A novel decision support model for satisfactory restaurants utilizing social information. Tourism Management, 59, 281–297. https://doi.org/https://doi.org/10.1016/j.tourman.2016.08.010
- Zhang, Z., Zhang, Z., & Yang, Y. (2016). The power of expert identity: How website-recognized expert reviews influence travelers’ online rating behavior. Tourism Management, 55, 15–24. doi:https://doi.org/10.1016/j.tourman.2016.01.004