政大機構典藏-National Chengchi University Institutional Repository(NCCUR):Item 140.119/50992

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政大典藏 > College of Informatics > Department of Computer Science > Theses > Item 140.119/50992

Please use this identifier to cite or link to this item: https://nccur.lib.nccu.edu.tw/handle/140.119/50992

Title:	利用維基百科及網路相簿從遊記探勘個人化旅遊行程 Mining personalized trip plan from travelogues using wikipedia and web albums
Authors:	吳容瑜
Contributors:	沈錳坤 Shan, Man Kwan 吳容瑜
Keywords:	工作流程
Date:	2010
Issue Date:	2011-09-29 18:25:09 (UTC+8)
Abstract:	近幾年國內的自助旅遊風氣逐漸盛行，行前準備對一般人而言必須花費不少時間。我們必須從網路上收集各方資料加以整理，再規劃出理想的行程，所以行程規劃非常耗時。因此，本篇論文研究由網友撰寫的旅遊文章中，利用維基百科和網路相簿作為輔助工具，探勘分析旅遊景點與路線，並根據使用者的需求提供個人化的行程安排，以作為行程規劃之參考。我們先將遊記標題透過中文斷詞系統進行斷詞，再利用維基百科確認是否為景點名稱。接著我們利用遊記作者權威性與景點重要性之間的相互強化關係來判斷每個景點的重要性。取得景點名稱及其重要性之後，我們利用遊記的文章結構與特性進而判斷遊記中的旅遊路線，由此步驟可得知景點在旅遊路線中的前後關係。此外，我們利用網路相簿中旅遊相片的資訊預估景點停留時間，並且從交通查詢網站取得景點間的交通時間。系統根據使用者給定的必經景點，推薦使用者符合條件的旅遊路線。接著，根據使用者選定的旅遊路線，考量使用者給定的時間限制、景點開放時間的限制與景點交通時間的限制，排定行程內各景點適合的參訪時間。最後，根據上述步驟的結果，系統便可推薦使用者個人化的行程。我們以國內知名BBS批踢踢實業坊上的日本旅遊看版內之遊記作為實驗資料來源，並參考維基百科及Flickr網路相簿，實作出個人化的旅遊行程推薦系統。實驗顯示本論文所萃取出的景點名稱，其精確度92％、召回率100%，而景點停留時間與Ground Truth的誤差範圍為3.16％。最後，滿意度評估顯示本論文的推薦系統符合個人化需求。 Trip planning is an important and time-consuming step for backpackers. Most research focuses on finding the travel sequence from different data sources such as blog, photos and GPS. Although these approaches can recommend some popular travel sequences for a tourist, but tourist’s place preferences and temporal constraints are not considered. In this thesis, we propose an approach for personalized trip planning which takes tourist’s preference and temporal constraint into consideration. In the proposed approach, first the place names are extracted from travelogues with the aid of the Wikipedia. Then the travel sequences are extracted from travelogues. Based on the relationship of mutual reinforcement between the authority of a place and the hub of a travelogue author, the authority of each place is derived. Moreover, the stay time of each place is estimated from the information of travel photos of a web album. Finally, based on the user specified place preference and temporal constraints, this thesis presents the algorithms to arrange a personalized trip for a user. The experiments show that the place name extraction achieves 92% precision and 100% recall. For the estimation of place stay time, the error is 3.16% compared with the ground truth collected from well-known backpacker site.
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Description:	碩士國立政治大學資訊科學學系 97753037 99
Source URI:	http://thesis.lib.nccu.edu.tw/record/#G0097753037
Data Type:	thesis
Appears in Collections:	[Department of Computer Science ] Theses

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