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系統識別號 U0002-2008201411110500
中文論文名稱 時間限制之旅遊景點推薦
英文論文名稱 Time-Constrained Scenic Spots Recommendation
校院名稱 淡江大學
系所名稱(中) 資訊工程學系碩士班
系所名稱(英) Department of Computer Science and Information Engineering
學年度 102
學期 2
出版年 103
研究生中文姓名 謝濬謙
研究生英文姓名 Chun-Chien Hsieh
學號 600411838
學位類別 碩士
語文別 中文
第二語文別 英文
口試日期 2014-07-24
論文頁數 67頁
口試委員 指導教授-許輝煌
委員-許輝煌
委員-施國琛
委員-王慶生
中文關鍵字 推薦系統  網路應用程式  GPS  戶外 
英文關鍵字 Recommendation system  Web App  GPS  Outdoor 
學科別分類 學科別應用科學資訊工程
中文摘要 隨著我國生活水準不斷提高,旅遊活動安排之頻率亦隨之提高,迄今已成為國人日常生活不可或缺之要素了。台灣被稱為福爾摩沙即是美麗之島之意思,因此面對國內這許多美景、古蹟、文化與藝術以及美食等特色之景點等,如何在旅遊前規劃該次主題旅遊或針對特定景點,在有限的時間內,連同周邊特色景點一併瀏覽,是一件值得開發與探究之主題。基此;本研究即使要建構一個務實之景點推薦系統,會主動偵測使用者所在位置,並在接收使用者所輸入旅遊時間後自動估算,推薦旅遊路線給使用者,該系統擬採用WebApp的形式來服務不同平台的使用者,透過無遠弗屆與便利之網際網路,讓想旅遊之國人,能在任何有網路的地方來使用這套系統。本系統是以新北市為測試標的,首先收集並彙整新北市各特色景點並依其行政區編撰旅遊景點資料庫,接著使用HTML5 Geolocation API,Geolocation API能讓伺服器藉由使用者裝置上的感應器定位使用者位置,再透過Google Distance Matrix API辨識最接近的行政區,此時系統查詢景點資料庫便會讀取這個行政區裡的旅遊景點,再次利用Distance Matrix計算使用者位置到景點以及景點之間的移動時間,最後根據使用者輸入的時間限制,把這些資料都傳送給推薦演算法。
我們的推薦演算法跟傳統的推薦系統有很大的差異,傳統的推薦方法常利用分析資料或是找出使用者間的關聯性來做推薦,但是這些方法無法解決我們的問題,因此我們採用了一個新的演算法,從使用者附近的旅遊景點裡進行搜索,尋找滿足使用者時間限制而且分數最高的旅遊路線,最後把包含花費時間最少的旅遊路線在內總共兩條路線推薦給使用者,根據實驗的結果,高於九成五的測試者願意使用我們的推薦系統進行屢有規劃。基此;本研究所開發之”時間限制之旅遊景點推薦系統”是一個值得用來規畫旅遊管理之好系統。
英文摘要 As the people's living quality improvement,the frequency of outdoor activities especially traveling was increased as well. The most headache problem of traveling is we don`t have enough time to visit all spots which we are interested. And we are also afraid to spend too much time in moving, especially the time was limited. If there is a inquire system could give us a suitable suggestion to plan our journey according our location and our time, it will be helpful for us to plan a good, a meaningful trip.
The goal of this study is to build a time-constrained scenic spots recommendation system of trip which is not only to search user`s location, but also to consider the traveling time of user, then give a recommend set of significant spots to users. Nowadays internet was well developed, we could build a WebApp system to service users from different platforms, and people could use this system in everywhere if the internet is available.
The New Taipei city was selected to be the test sample for system, and to collect and integrate the character spots of this city was the first step of this system. Then we compile the scenic spots data bank of this city based on the district of previous spots.
In this system, HTML5 Geolocation API could help us to locate users, and to know which district is nearest by Google Distance Matrix API. Then inquiry system will identify all spots in this district, and estimate the moving time from one scenery to another by Distance Matrix. Finally, Base on the previous information and the available traveling time of user, the system will deductive the best two sightseeing way to user, including the minimum spent time for touring.
According the feedback of real test, we got highly recognized,and more than 95% tester expressed that they will use this system to schedule their travel plan. Obviously; time-constrained scenic spots recommendation system is worthy to be a tour management system.
論文目次 目錄
第一章 緒論 1
1.1 研究背景與動機 1
1.2 研究目的 3
1.3 論文組織與架構 4
第二章 文獻探討 6
2.1 網路應用程式 6
2.1.1 HTML5 7
2.1.2 Geolocation API 8
2.2 推薦機制 9
2.2.1 基於內容推薦 9
2.2.2 協同過濾推薦 10
2.2.3 基於關聯規則推薦 13
2.2.4 其他推薦 14
第三章 系統架構與方法 17
3.1 系統架構 17
3.2 移動時間估算 21
3.2.1 直線距離 21
3.2.2 Google Distance Matrix API 22
3.3 景點資料庫 27
3.4 定位方法 29
3.5 行政區判定 30
3.6 推薦演算法 31
第四章 實驗結果與分析 37
4.1 網頁伺服器 37
4.2 系統流程 39
4.3 實驗結果分析 45
4.3.1 推薦結果與討論 45
4.3.2 使用者回饋 53
第五章 結論 56
5.1 結論 56
5.2 未來展望 56
參考文獻 58
附錄一 系統滿意度問卷 60
附錄二 英文論文 61

圖目錄
圖1 瀏覽器詢問是否可以取得位置資訊 8
圖2 協同過濾原理 11
圖3 環境架構圖 18
圖4 系統流程圖 20
圖5 Google Maps 22
圖6 宣告HTML5 24
圖7 載入Google Maps API 24
圖8 DistanceMatrix 運作架構 26
圖9 字串處理流程 27
圖10 新北市行政區地圖[19] 28
圖11 行政區判斷架構圖 31
圖12 地圖範例 32
圖13 演算法流程圖 35
圖14 AppServ安裝畫面 38
圖15 安裝成功之後的主頁 38
圖16 HTML5推薦系統首頁 39
圖17 判斷行政區頁面範例 40
圖18 詢問旅遊時間介面 41
圖19 選擇推薦方法 41
圖20 選擇景點範例 42
圖21 推薦方法1範例 43
圖22 推薦方法2範例 44
圖23 水碓街位置判定 45
圖24 水碓街46巷行政區判定 46
圖25 頂溪站位置判定 46
圖26 頂溪站行政區判定 47
圖27 江子翠站位置判定 47
圖28 江子翠站行政區判定 48
圖29 系統便利性回饋結果統計圖 53
圖30 推薦結果回饋統計圖 54
圖31 執行速度回饋統計圖 55

表目錄
表1 Google Maps JavaScript API服務項目[17] 23
表2 Google Maps地圖設定[17] 25
表3 getCurrentPosition()可取得的資料及設定[20] 29
表4 水碓街46巷的推薦結果 49
表5 頂溪站的推薦結果 50
表6 江子翠站的推薦結果 51
參考文獻 [1] 中華民國交通部觀光局 http://admin.taiwan.net.tw
/index.aspx(last accessed April 18, 2014)
[2] 旅遊資訊王http://travel.network.com.tw(last accessed April 21, 2014)
[3] 易遊網http://www.eztravel.com.tw/package1/taiwan
_tsm.htm?grt=go01(last accessed April 21, 2014)
[4] Alex MacCaw.2011.JavaScript Web Applications.O'Reilly Media Formats:
[5] Bruce Lawson, Remy Sharp.2011.Introducing HTML5.2nd Edition. New Riders
[6] “Development of Trip Planning Systems on Public Transit in Taiwan” Jau-Ming Su ; Chung Hua Univ.; Hsinchu Chih-Hung Chang ; Wen-Chi Ho, Networking, Sensing and Control, 2008. ICNSC 2008. IEEE International Conference on pp. 791 – 795, April 2008
[7] HTML5 compatibility on mobile and tablet browsers with testing on real deviceshttp://mobilehtml5.org/ (last accessed April 25, 2014)
[8] Michael J. Pazzani and Daniel Billsus 2007. Content-Based Recommendation Systems. In ”The Adaptive Web” ,325-341, Springer-Verlag Berlin Heidelberg
[9] Michael D. Ekstrand, John T. Riedl, Joseph A. Konstan. 2011.Collaborative Filtering Recommender Systems.Now Publishers Inc
[10] 推薦系統:主要推薦方法http://zhousen.zju.blog.163.com/blog/static/
1802920090179422912(last accessed April 28, 2014)
[11] Jean-Marc Adamo.2001.Data Mining for Association Rules and Sequential Patterns: Sequential and Parallel Algorithms.Springer Science & Business Media
[12] “Trip-Mine: An Efficient Trip Planning Approach with Travel Time Constraints” E. H. -C. Lu, Chih-Yuan Lin and V. S. Tseng, 2011 12th IEEE International Conference on Mobile Data Management (MDM), Vol.1, pp.152–161, June.2011.
[13] “Design of an Intelligent Route Planning System using an Enhanced A*-search Algorithm” Wong Poh Lee, Minden,Osman, M.A. ; Sabudin, M. 2009 Third Asia International Conference on Modelling & Simulation pp.40–44,May.2009
[14] “An Algorithm for Trip Planning with Constraint of Transfer Connection in Urban Mass Transit Network” Jianyuan Guo; Limin Jia ; Jie Xu; Yong Qin; Distributed Computing and Applications to Business, Engineering & Science (DCABES), 2012 11th International Symposium on pp.341–344, Oct. 2012
[15] “An Efficient Trip Planning Algorithm under Constraints” Jinling Bao ;Xiaochun Yang ; Bin Wang ; Jiaying Wang; Web Information System and Application Conference (WISA), 2013 10th; pp.429–434, Nov. 2013
[16] Jarvis, Jeff .2009.What Would Google Do?.Harpercollins
[17] Gabriel Svennerberg.2010.Beginning Google Maps API 3.2 edition.Apress;
[18] Google Maps JavaScript API 第3版https://developers.google
.com/maps/documentation/javascript/tutorial?hl=zh-tw#Audience( last accessed May 10, 2014)
[19] 新北市政府http://www.ntpc.gov.tw/ch/index.jsp
[20] w3schoolhttp://www.w3school.com.cn/index.html ( last accessed May 12, 2014)
[21] “The Effects of Service Quality on Customer Relational Benefits in Travel Website”Chi-Shiun Lai;Chun-Shou Chen; Pei-June Lin; Portland International Center for Management of Engineering and Technology; pp.1133 – 1140, Aug. 2007
[22] “Internet Marketing and Innovative Strategies: A Study of China's Travel Agencies”Cheng Congxi;Xiang Lan ; Geng Pengfei;2010 International Conference on Management and Service Science (MASS);pp.1 – 4, Aug. 2010
[23] “A study of influencing factors of urban residents' trip mode in metropolises based on structure model” Chunhui Huang; 2011 International Conference on Remote Sensing, Environment and Transportation Engineering (RSETE); pp. 5677 – 5680;June 2011
[24] “Study on Urban Design Strategy for Low-carbon Trip”Zhao Hongyu;Chu Yun; ICEET '09. International Conference on Energy and Environment Technology, 2009 (Volume:1 );pp.373 – 377,Oct. 2009
[25] “A Novel Loglinear Model for Freeway Travel Time Prediction”Lili Huang;Barth, M.;11th International IEEE Conference on Intelligent Transportation Systems; pp.210 – 215;Oct. 2008
[26] “A Personalised Online Travel Time Prediction Model”Zhenchen Wang; Poslad, S.; 2013 IEEE International Conference on Systems, Man, and Cybernetics (SMC); pp.3327 – 3332; Oct. 2013
[27] “Analysis of Travel Time Patterns in Urban Using Taxi GPS Data”Mengdan Gao;Tongyu Zhu ; Xuejin Wan ; Qi Wang;2013 IEEE and Internet of Things (iThings/CPSCom) Green Computing and Communications (GreenCom), IEEE International Conference on and IEEE Cyber, Physical and Social Computing;pp.512 – 517;Aug. 2013
[28] “Design and implementation of an open data assisted real-time trip planner”Tien-Yu Chu;Kun-Che Hsu ; Jenq-Shiou Leu;In 2014 International Conference on telligent Green Building and Smart Grid (IGBSG); pp.1 – 4;April 2014
[29] “LEARNING THE TRIP SUGGESTION FROM LANDMARK PHOTOS ON THE WEB”Rongrong Ji;Ling-Yu Duan ; Jie Chen ; Shuang Yang ; Hongxun Yao ; Tiejun Huang ; Wen Gao;2011 18th IEEE International Conference on Image Processing (ICIP); pp.2485 – 2488;Sept. 2011
[30] “Smart Travel Planner: A mashup of travel-related web services”Jafri, R.;Alkhunji, A.S. ; Alhader, G.K. ; Alrabeiah, H.R. ; Alhammad, N.A. ; Alzahrani, S.K.;2013 International Conference on Current Trends in Information Technology (CTIT);pp.181 – 185;Dec. 2013
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