Prediction of User Overall Gratification in Indian Tourism Domain on Hotel classes and Trip-Types

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Published Sep 8, 2021
Venkata daya sagar Ketaraju

Abstract

The revenue and economy of the country in the past years significantly depend on tourism. The hotel sector's role is even more prominent in tourism. The plans and decisions of tours of users can be recommended with the collaboration of E-commerce and hotel management. The traveling proportion of the population is getting minor over the months due to the worst impact of COVID-19. Thus not just the tourism, the hotel sector is also in vain in terms of revenue. Users' past experiences and opinions help boost their satisfaction levels by providing recommendations and retaining them. The present scenario and stats prove that the selection and decision of hotels have enormous support on user reviews. This research article tries to find and analyze the various aspects that contribute more towards the gratification levels of users in Indian top tourism city hotels listed by the Master and VISA Inc survey. This survey focuses on the item-item collaborative filtering and regression techniques based on TripAdvisor reviews of recent times. Once the dimensions are known, it helps in improving them and thus even enhances the ratings of Asian continental hotel management. This study proves that the online travel platform helps obtain reviews from users to maintain the travel recommender systems.

How to Cite

Ketaraju, V. daya sagar. (2021). Prediction of User Overall Gratification in Indian Tourism Domain on Hotel classes and Trip-Types. SPAST Abstracts, 1(01). Retrieved from https://spast.org/techrep/article/view/224
Abstract 5 |

Article Details

Keywords

recommender system, collaborative filtering, VISA, e-commerce

References
1. [1].Bissell, D. (2012). Mobile testimony in the information age: The powers of travel reviews. International Journal of Cultural Studies, 15(2), 149–164. doi:10.1177/ 1367877911416885

2. [2] Chatterjee, P. (2001). Online Reviews: Do Consumers Use Them? In M.C. Gilly & J. Myers- Levy (Eds.), Proceedings of the A.C.R. 2001, pp. 129-134. Provo, UT: Association for Consumer Research.

3. [3]. Chinta Venkata Murali Krishna, Dr. G. Appa Rao. "Acquiring the user's opinion by using a generalized Context-aware Recommender System for real-world applications," International Journal of Engineering & Technology, 2018.

4. [4]. Chinta Venkata Murali Krishna, Dr. G. Appa Rao, Dr. S.AnuRadha "A Framework For The Identification Of Significant Contexts In Tourism Domain," International Journal of Advanced Science and Technology Vol. 29, No. 7, (2020), pp. 1007-1029.

5. [5]. .Dellarocas, C. (2003). The Digitization of Word-Of-Mouth: Promise and Challenges of Online Feedback Mechanisms. Management Science, 49 (10), 1407-1424.

6. [6]. Filieri, R., and F. McLeay. 2014. EWOM and accommodation an analysis of the factors that influence travelers' adoption of information from online reviews. J. Travel Res. 53 (1): 44-57.

7. [7]. Gretzel, U. (2006). Consumer Generated Content: Trends and Implications for Branding, e- Review of Tourism Research (eRTR), 4 (3).

8. [8]. Gretzel, U, Hyan-Yoo, K, and Purifoy, M (2007) Online Travel Review Study: The role and impact of online travel reviews. Laboratory for Intelligent Systems in Tourism, College Station

9. [9]. Inversini, A., and I. Masiero. 2014. Selling rooms online: the use of social media and online travel agents. Int. J. Hosp. Manag. 26 (2): 272-292.

10. [10]. Pew Internet & American Life Project (2006b). Internet Activities. Accessed online (December 1,2006) at: http://www.pewlnternet.Qr.ij/trends/Internet Activities 7- ^9.06.htm

11. [11]. .Xiang, Z., & Gretzel, U. (2010). Role of social media in online travel information search. Tourism Management, 31(2), 179–188. doi:10.1016/j.tourman.2009.02.016

12. [12] Bala Brahmeswara Kadaru, B.Raja Srinivasa Reddy.," A novel ensemble decision tree classifier using hybrid feature selection measures for Parkinson's disease prediction", "Int.J.Data Science", Vol.3.No.4(2018),pp.289-307.

13. Bala Brahmeswara Kadaru, B.Raja Srinivasa Reddy." An improved parallel PSO-FS based N-SVM technique for medical disease prediction", "Jour of Adv Research in Dynamical & Control Systems", Vol.10(2018).pp.723-737.
Section
GE3- Computers & Information Technology