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dc.contributor.author顏重恩zh_TW
dc.contributor.author黃明居zh_TW
dc.contributor.authorYen,Chung-Enen_US
dc.contributor.authorHwang ,Ming-Jiuen_US
dc.date.accessioned2018-01-24T07:41:37Z-
dc.date.available2018-01-24T07:41:37Z-
dc.date.issued2017en_US
dc.identifier.urihttp://etd.lib.nctu.edu.tw/cdrfb3/record/nctu/#GT070453612en_US
dc.identifier.urihttp://hdl.handle.net/11536/142005-
dc.description.abstract國際郵輪市場快速發展,近年來亞洲搭乘郵輪的人數逐年增加,目前交通部規劃未來國際郵輪發展會以基隆港為主要的靠泊港,當郵輪停泊港口之後,自由行旅客便會在基隆和台北等地區進行岸上旅遊。但因為外籍旅客對於台灣觀光資訊來源不足,對於台灣旅遊景點的認識不夠,台灣港口也缺少提供旅遊資訊的設備和系統,因此外籍旅客很難在短時間內尋找到符合自己喜好的觀光旅遊景點。 本研究使用協同式推薦法建立郵輪岸上旅遊推薦系統,此系統可以幫助來台自由行的郵輪旅客減少搜尋旅遊景點的時間,藉由系統推薦出符合旅客偏好的景點,並使用基因演算法規劃旅遊路線,實證研究顯示,本研究所提出的推薦模式與系統可以提供郵輪自由行旅客較佳的方便性與滿意度。zh_TW
dc.description.abstractNowadays, the international cruise market develops rapidly. The number of people taking cruises to Asia increases gradually. The Taiwan Ministry of Transportation and Communication has planned Keelung port to be the main international port of call. After cruises stop at Keelung port, individual tourists will travel around Taipei and Keelung. Because some of the foreign tourists don’t have enough tourist information of Taiwan or don’t know much about Taiwan, it is not easy for them to find attractions which match their preference. Keelung port also lacks of the information equipment and systems. In this research, we will use collaborative filtering to build a tourism recommendation system for cruise individual passengers. After using this system, cruise individual passengers can save time for finding attractions. The system will recommend attractions which match their preference and plan a tour through genetic algorithm. According to our results, our recommendation model and system can provide more convenient and satisfying tour planning services.en_US
dc.language.isozh_TWen_US
dc.subject郵輪旅遊zh_TW
dc.subject旅遊規劃zh_TW
dc.subject推薦系統zh_TW
dc.subjectCruiseen_US
dc.subjectTour planningen_US
dc.subjectRecommendation Systemen_US
dc.title郵輪旅客岸上旅遊推薦系統之研究zh_TW
dc.titleA study of cruise passenger tourism recommendation systemen_US
dc.typeThesisen_US
dc.contributor.department運輸與物流管理學系zh_TW
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