{"title":"gTravel:为一群游客提供天气感知POI推荐引擎","authors":"Rajani Trivedi, Bibudhendu Pati, Subhendu Kumar Rath","doi":"10.13053/cys-27-3-4550","DOIUrl":null,"url":null,"abstract":"Weather is a big factor in tourist decisions, andcertain places or events aren’t even recommendedduring dangerously bad weather. It is difficult to providea better recommendation to a group of tourists in thesecircumstances. We propose gTravel, a weather assistantframework that predicts weather in specified pointsof interest for a group of tourists. We demonstratehow prior knowledge of climatic patterns at a POI,as well as prior insights into how visitors rank theirdestinations in a variety of weather conditions, can helpimprove choice reliability. According to our findings, therecommendations are significantly more valid, and therecommended remedy is more comfortable.","PeriodicalId":333706,"journal":{"name":"Computación Y Sistemas","volume":"10 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2023-09-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"gTravel: Weather-Aware POI Recommendation Engine for a Group of Tourists\",\"authors\":\"Rajani Trivedi, Bibudhendu Pati, Subhendu Kumar Rath\",\"doi\":\"10.13053/cys-27-3-4550\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Weather is a big factor in tourist decisions, andcertain places or events aren’t even recommendedduring dangerously bad weather. It is difficult to providea better recommendation to a group of tourists in thesecircumstances. We propose gTravel, a weather assistantframework that predicts weather in specified pointsof interest for a group of tourists. We demonstratehow prior knowledge of climatic patterns at a POI,as well as prior insights into how visitors rank theirdestinations in a variety of weather conditions, can helpimprove choice reliability. According to our findings, therecommendations are significantly more valid, and therecommended remedy is more comfortable.\",\"PeriodicalId\":333706,\"journal\":{\"name\":\"Computación Y Sistemas\",\"volume\":\"10 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2023-09-29\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Computación Y Sistemas\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.13053/cys-27-3-4550\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Computación Y Sistemas","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.13053/cys-27-3-4550","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
gTravel: Weather-Aware POI Recommendation Engine for a Group of Tourists
Weather is a big factor in tourist decisions, andcertain places or events aren’t even recommendedduring dangerously bad weather. It is difficult to providea better recommendation to a group of tourists in thesecircumstances. We propose gTravel, a weather assistantframework that predicts weather in specified pointsof interest for a group of tourists. We demonstratehow prior knowledge of climatic patterns at a POI,as well as prior insights into how visitors rank theirdestinations in a variety of weather conditions, can helpimprove choice reliability. According to our findings, therecommendations are significantly more valid, and therecommended remedy is more comfortable.