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

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. © The Electrochemical Society

Collaborative Filtering and Regression Techniques based location Travel Recommender System based on social media reviews data due to the COVID-19 Pandemic

Nowadays, Dynamic industries like tourism is a enhance to boost many countries economy in recent years. The hotel sector leads to a significant role among all aspects of the tourism industry. Online travel platforms, in association with hotel management, are a part of hotel E-tourism that helps users to make travel plans online, suggest precise recommendations in consideration with the earlier feedbacks upon hotel stay. From the past few months, the user roaming ratio falls rapidly due to the COVID-19 Pandemic across the globe. In a business concern, this Pandemic affects all tourism sectors, specifically in the hotels' occupancy. To retain business positions and attract the users in these problematic scenarios, hotel and travel platform management concerns about past preferences of users, which have a significant impact on gratification for a comfortable stay. The current market trends show that user reviews are playing a vital role to select a hotel based on a safe location. Our work tries to find and recommend the dimensions that contribute more towards higher satisfaction levels of users in Asian continental top tourism city hotels listed by the Master and VISA Inc survey with item collaborative filtering and regression techniques based on Tripadvisor reviews from social media in recent times. The outcome of the work is helpful for Asian continental hotel management to concentrate more on the dimensions for obtaining better reviews. From an online travel platform perspective, this work suggests the contexts for the travel recommender system to gain things effectively from users. © Published under licence by IOP Publishing Ltd.