| Journal: | Computación y sistemas |
| Database: | |
| System number: | 000560824 |
| ISSN: | 1405-5546 |
| Authors: | Trivedi, Rajani1 Pati, Bibudhendu1 Rath, Subhendu Kumar2 |
| Institutions: | 1Rama Devi Women’s University, Bhubaneswar. India 2Biju Patnaik University Of Technology, Rourkela, Orissa. India |
| Year: | 2023 |
| Season: | Jul-Sep |
| Volumen: | 27 |
| Number: | 3 |
| Pages: | 667-674 |
| Country: | México |
| Language: | Inglés |
| English abstract | Weather is a big factor in tourist decisions, and certain places or events aren’t even recommended during dangerously bad weather. It is difficult to provide a better recommendation to a group of tourists in these circumstances. We propose gTravel, a weather assistant framework that predicts weather in specified points of interest for a group of tourists. We demonstrate how prior knowledge of climatic patterns at a POI, as well as prior insights into how visitors rank their destinations in a variety of weather conditions, can help improve choice reliability. According to our findings, the recommendations are significantly more valid, and the recommended remedy is more comfortable. |
| Keyword: | POI, Tourist, Weather, Recommendation, Interest |
| Full text: | Texto completo (Ver HTML) Texto completo (Ver PDF) |
