F. Colace, M. D'arienzo, Angelo Lorusso, Marco Lombardi, D. Santaniello, Carmine Valentino
{"title":"A Novel Context Aware Paths Recommendation Approach for the Cultural Heritage Enhancement","authors":"F. Colace, M. D'arienzo, Angelo Lorusso, Marco Lombardi, D. Santaniello, Carmine Valentino","doi":"10.1109/SMARTCOMP58114.2023.00071","DOIUrl":null,"url":null,"abstract":"The will to travel leads humans to discover new places and enjoy new adventures. However, tourists usually need help knowing what to visit and, avoiding time issues, in which order to explore several Points of Interest (POIs). In this field, new technologies can help tourists to improve their experiences and select the visiting path according to personal preferences. Therefore, the employment of Recommender Systems allows the personalization of the experience through the appropriate POIs’ selection. Moreover, RSs’ analysis could take advantage of contextual information that suits the personalization in the specific environment where the elaboration happens, providing users with even more specific and tailored paths. This paper aims to design personalized visiting paths combining a Context-Aware Recommender System (CARSs) and a mathematical model to maximize the number of visited POIs in the available time. The proposed approach is tested through a prototype, obtaining promising results.","PeriodicalId":163556,"journal":{"name":"2023 IEEE International Conference on Smart Computing (SMARTCOMP)","volume":"4 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2023-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2023 IEEE International Conference on Smart Computing (SMARTCOMP)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/SMARTCOMP58114.2023.00071","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
Abstract
The will to travel leads humans to discover new places and enjoy new adventures. However, tourists usually need help knowing what to visit and, avoiding time issues, in which order to explore several Points of Interest (POIs). In this field, new technologies can help tourists to improve their experiences and select the visiting path according to personal preferences. Therefore, the employment of Recommender Systems allows the personalization of the experience through the appropriate POIs’ selection. Moreover, RSs’ analysis could take advantage of contextual information that suits the personalization in the specific environment where the elaboration happens, providing users with even more specific and tailored paths. This paper aims to design personalized visiting paths combining a Context-Aware Recommender System (CARSs) and a mathematical model to maximize the number of visited POIs in the available time. The proposed approach is tested through a prototype, obtaining promising results.