{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,2]],"date-time":"2025-10-02T10:38:39Z","timestamp":1759401519295,"version":"3.40.5"},"reference-count":23,"publisher":"Wiley","license":[{"start":{"date-parts":[[2021,3,16]],"date-time":"2021-03-16T00:00:00Z","timestamp":1615852800000},"content-version":"unspecified","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Mobile Information Systems"],"published-print":{"date-parts":[[2021,3,16]]},"abstract":"<jats:p>In view of the fact that the important characteristics of tourism destination selection preference are not considered in the current prediction methods of tourism destination selection preference, resulting in low prediction accuracy and comprehensive accuracy and long prediction time, a tourism destination selection preference prediction method based on edge calculation is proposed. This paper uses edge computing to construct the characteristics of tourism destination selection preference and uses a random forest algorithm to select important features and carry out preliminary estimation and ranking. Using the multiple logit selection model, the tourists\u2019 preference sequence for tourism destination selection is obtained and sorted and the tourism destination selection preference model is obtained. By calculating the weight value of tourism destination selection preference, the weight set of tourism destination selection preference is determined and the tourism destination selection preference is determined according to the link prediction method to realize the tourism destination selection preference prediction. The experimental results show that the comprehensive accuracy of the proposed method is good, which can effectively improve the prediction accuracy of tourism destination selection preference and shorten the prediction time of tourism destination selection preference.<\/jats:p>","DOI":"10.1155\/2021\/5512008","type":"journal-article","created":{"date-parts":[[2021,3,17]],"date-time":"2021-03-17T22:20:10Z","timestamp":1616019610000},"page":"1-11","source":"Crossref","is-referenced-by-count":10,"title":["Tourism Destination Preference Prediction Based on Edge Computing"],"prefix":"10.1155","volume":"2021","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-3354-0575","authenticated-orcid":true,"given":"Bin","family":"Deng","sequence":"first","affiliation":[{"name":"Lyceum of the Philippines University, Batangas 4200, Philippines"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jun","family":"Xu","sequence":"additional","affiliation":[{"name":"Lyceum of the Philippines University, Batangas 4200, Philippines"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xin","family":"Wei","sequence":"additional","affiliation":[{"name":"Lyceum of the Philippines University, Batangas 4200, Philippines"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"311","reference":[{"key":"1","doi-asserted-by":"publisher","DOI":"10.1016\/j.ecolecon.2019.106589"},{"issue":"1","key":"2","first-page":"64","article-title":"Simulation study on impacts of high aviation carbon taxes on tourism: application of portfolio vacation choice model","volume":"2449","author":"V. C. Sander","year":"2018","journal-title":"Transportation Research Record"},{"key":"3","doi-asserted-by":"publisher","DOI":"10.1007\/s00484-016-1220-1"},{"issue":"1","key":"4","doi-asserted-by":"crossref","first-page":"51","DOI":"10.3141\/2343-07","article-title":"Day-to-Day origin\u2013destination tuple estimation and prediction with hierarchical bayesian networks using multiple data sources:","volume":"2343","author":"Y. Ma","year":"2018","journal-title":"Transportation Research Record"},{"key":"5","doi-asserted-by":"publisher","DOI":"10.1109\/tits.2017.2749413"},{"key":"6","doi-asserted-by":"publisher","DOI":"10.3745\/jips.03.0063"},{"key":"7","doi-asserted-by":"publisher","DOI":"10.3233\/ifs-151905"},{"key":"8","doi-asserted-by":"publisher","DOI":"10.1049\/iet-rpg.2015.0065"},{"issue":"2","key":"9","doi-asserted-by":"crossref","first-page":"212","DOI":"10.1049\/iet-rpg.2015.0065","article-title":"Temperature prediction of the molten salt collector tube using BP neural network","volume":"10","author":"T. Ren","year":"2020","journal-title":"IET Renewable Power Generation"},{"key":"10","doi-asserted-by":"publisher","DOI":"10.1016\/j.jenvman.2019.109458"},{"key":"11","doi-asserted-by":"crossref","first-page":"91","DOI":"10.3141\/2322-10","article-title":"Dynamic analysis of Japanese tourists\u2019 three stage choices: tourism participation, destination choice, and travel mode choice","volume":"2322","author":"L. Wu","year":"2018","journal-title":"Transportation Research Record"},{"key":"12","doi-asserted-by":"publisher","DOI":"10.1109\/TITS.2020.3017596"},{"issue":"99","key":"13","first-page":"1","article-title":"Modelling taxi drivers' behaviour for the next destination prediction","author":"A. Rossi","year":"2019","journal-title":"IEEE Transactions on Intelligent Transportation Systems"},{"key":"14","article-title":"Multiple discrimination and pairwise CNN for view-based 3D object retrieval","author":"Z. Gao","year":"2020","journal-title":"Neural Networks"},{"key":"15","doi-asserted-by":"publisher","DOI":"10.1109\/tits.2019.2924903"},{"issue":"4","key":"16","doi-asserted-by":"crossref","first-page":"2910","DOI":"10.1109\/TII.2020.2987994","article-title":"Edge server quantification and placement for offloading social media services in industrial cognitive IoV","volume":"17","author":"X. Xu","year":"2020","journal-title":"IEEE Transactions on Industrial Informatics"},{"key":"17","first-page":"1","article-title":"Spatiotemporal modeling and real-time prediction of origin-destination traffic demand","volume":"63","author":"X. Xian","year":"2019","journal-title":"Technometrics"},{"key":"18","doi-asserted-by":"publisher","DOI":"10.32604\/cmes.2020.09463"},{"key":"19","doi-asserted-by":"publisher","DOI":"10.1587\/transinf.2018edp7299"},{"key":"20","doi-asserted-by":"publisher","DOI":"10.1007\/s11116-018-9896-1"},{"issue":"1","key":"21","doi-asserted-by":"crossref","first-page":"39","DOI":"10.3141\/2085-05","article-title":"Dynamic origin-destination demand estimation using turning movement counts:","volume":"2085","author":"H. Alibabai","year":"2018","journal-title":"Transportation Research Record"},{"issue":"4","key":"22","first-page":"356","article-title":"Modeling and simulation analysis of QoE perception of network quality based on user preferences","volume":"37","author":"G. Feng","year":"2020","journal-title":"Computer Simulation"},{"issue":"8","key":"23","doi-asserted-by":"crossref","first-page":"3219","DOI":"10.1109\/TITS.2019.2924971","article-title":"Deep multi-scale convolutional LSTM network for travel demand and origin-destination predictions","volume":"21","author":"K. F. Chu","year":"2019","journal-title":"IEEE Transactions on Intelligent Transportation Systems"}],"container-title":["Mobile Information Systems"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/downloads.hindawi.com\/journals\/misy\/2021\/5512008.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/downloads.hindawi.com\/journals\/misy\/2021\/5512008.xml","content-type":"application\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/downloads.hindawi.com\/journals\/misy\/2021\/5512008.pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2021,3,17]],"date-time":"2021-03-17T22:20:20Z","timestamp":1616019620000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.hindawi.com\/journals\/misy\/2021\/5512008\/"}},"subtitle":[],"editor":[{"given":"Hsu-Yang","family":"Kung","sequence":"additional","affiliation":[],"role":[{"role":"editor","vocabulary":"crossref"}]}],"short-title":[],"issued":{"date-parts":[[2021,3,16]]},"references-count":23,"alternative-id":["5512008","5512008"],"URL":"https:\/\/doi.org\/10.1155\/2021\/5512008","relation":{},"ISSN":["1875-905X","1574-017X"],"issn-type":[{"type":"electronic","value":"1875-905X"},{"type":"print","value":"1574-017X"}],"subject":[],"published":{"date-parts":[[2021,3,16]]}}}