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研究生: 劉書宇
Liu, Shu-Yu
論文名稱: 以情境感知資料與社群資訊建構餐廳推薦系統之研究
A Study on Restaurant Recommendation Systems Using Context-aware Data and Social Information
指導教授: 林均翰
Lin, Chun-Han
學位類別: 碩士
Master
系所名稱: 資訊工程學系
Department of Computer Science and Information Engineering
論文出版年: 2016
畢業學年度: 104
語文別: 中文
論文頁數: 67
中文關鍵詞: FacebookGraph API情境感知推薦系統
英文關鍵詞: Facebook, Graph API, Context-aware, Recommendation System
DOI URL: https://doi.org/10.6345/NTNU202204464
論文種類: 學術論文
相關次數: 點閱:156下載:11
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  • 目前市面上大多的餐廳推薦系統當中所給予使用者的推薦資訊,皆透過開發者收集(網路相關資料、部落客體驗文章),或經由系統平台上的使用者共同分享上傳而成的資料集。但這些資料會隨著時間長久的累積,亦或是開發人員對於資料沒有定期更新或維護,即造成了資料集過於老舊以及維護需求人力成本的問題。
    本研究旨在藉由Facebook社群網站平台所提供的Graph API查詢語言技術,擷取其上的社群粉絲專頁餐廳資訊當作資料集,建構一套與Facebook同步的即時動態更新的餐廳推薦系統。此外本研究也結合情境感知(Context-aware)方式來開發應用,讓系統服務更能貼近使用者,在情境資料上利用使用者情境資料(Facebook帳號資訊、偏好餐廳設定)及實體情境資料(時間、地理位置)和使用者的Google Calendar事件;而推薦功能部分,本系統透過使用者對於個別餐廳頁面的評分紀錄,作推薦過濾的排序演算,其中也用到Facebook的按讚數,當作計算因素的依據,讓推薦達到更有效的過濾。最後,本系統實作採用響應式網站設計來建構一個可以在電腦、手機及平板都具有良好瀏覽效果的網站平台雛型。

    Most traditional restaurant recommendation systems usually gather recommend information (i.e. internet source or blog article) from developers or members shared and uploaded to the system platform. These dataset probably are old and with potential maintain problems, which are brought about mainly by the increase of the dataset or by the lacking regularly update and maintenance.
    The study is aimed to develop a recommendation system by using Facebook released Graph API query language to retrieve page information about restaurants. Our system can be synchronized with the real-time information in Facebook. Additionally, we develop context-aware functions to provide convenient services for users. The context information includes user context (Facebook of user profile and preference setup), physical context (location and time) and user Google Calendar events. In this system, we use user rating scores of the restaurant and Facebook Page “likes” amounts as the filtering results to rate the recommendation list of the system. Finally, we implement a prototype website using Responsive Web Design methodology to support good user experiences on our web page for all devices including desktops, tablets, and phones.

    第一章 緒論 1 1.1 研究背景 1 1.2 研究動機與目的 3 1.3 論文架構 4 第二章 文獻探討 5 2.1 Facebook Graph API 5 2.2 情境感知 8 2.3 推薦系統 10 2.4 JSON 12 2.5 響應式網站設計 13 第三章 系統規劃與設計方法 17 3.1 Facebook社群資料收集 18 3.2 情境感知服務 21 3.3 推薦過濾評分計算 27 3.4 建構社群餐廳推薦系統 34 3.5 響應式網站設計 36 第四章 系統實作與應用 39 4.1 系統架構 40 4.1.1 系統架構圖 40 4.1.2 使用案例圖 41 4.1.3 循序圖 42 4.2 工具與平台使用 46 4.2.1 開發環境 47 4.2.2 Facebook APP 註冊 47 4.2.3 Google APP 註冊 49 4.3 實作畫面 52 4.3.1 系統登入 52 4.3.2 瀏覽餐廳資訊及登入社群資料 54 4.3.3 個別餐廳頁面及使用者評分 56 4.3.4 依使用者情境資訊推薦 57 4.3.5 依 Google Calendar 事件推薦 59 4.3.6 依使用者打卡資訊推薦 62 第五章 結論與未來展望 63 5.1 結論 63 5.2 未來展望 64 參考文獻 65

    [1] 蘇文彬, “Facebook全球活躍用戶第二季增至14.9億,8成8來自行動裝置”,iThome, http://www.ithome.com.tw/news/97849 , 2015, [存取日期: 8,2015]
    [2] 創市際市場研究顧問公司, “2014-15年台灣上網行為研究報告” , Rocket Café 火箭科技評論網, http://rocket.cafe/talks/53766, 創市際市場研究顧問, http://www.ixresearch.com/reports/cati , 2016, [存取日期:3, 2016]
    [3] 郭子瑜, “無所不在的個人化情境感知服務” ,數位典藏與學習電子報, http://newsletter.teldap.tw/news/InsightReportContent.php?nid=4395&lid=498, 2011, [存取日期:9, 2015]
    [4] Wikipedia, "協同過濾," https://zh.wikipedia.org/wiki/%E5%8D%94%E5%90%8C%E9%81%8E%E6%BF%BE, 2016, [存取日期:2, 2016]
    [5] “CSS3 Media Queries瀏覽器詳細支援狀況”, http://caniuse.com/css-mediaqueries, 2016, [存取日期:3, 2016]
    [6] Facebook Developer Documentation, “Graph API Overview”, https://developers.facebook.com/docs/graph-api/overview, 2015, [access time: 9, 2015]
    [7] Facebook Developer Documentation, “Access tokens -Facebook”, https://developers.facebook.com/docs/facebook-login/access-tokens/, 2015, [access time: 10, 2015]
    [8] Schilit, B.N. and Theimer, M.M., “Disseminating Active Map Information to Mobile Hosts.”, IEEE Network 8, 1994.
    [9] Schilit, B.N. Adams, and R. Want., “Context-aware computing applications.” , IEEE Workshop on Mobile Computing Systems and Applications Mobile Computing, Santa Cruz, CA, US.,1994
    [10] J. B. Schafer, J. Konstan, and J. Riedi, "Recommender systems in e-commerce," presented at the Proceedings of the 1st ACM conference on Electronic commerce, Denver, Colorado, United States, 1999.
    [11] D. Goldberg, D. Nichols, B. M. Oki, and D. Terry, "Using collaborative filtering to weave an information tapestry," Communcation of the ACM, 1992.
    [12] Badrul Sarwar, George Karypis, Joseph Konstan, and John Riedl, "Item-based Collaborative Filtering Recommendation Algorithms," GroupLens Research Group/Army HPC Research Center, 2001.
    [13] JSON, ” Introducing JSON”, http://www.json.org/, [access time: 9, 2015]
    [14] Marcotte, Ethan. “A List Apart: Articles: Responsive Web Design.”, 2010.
    [15] Gillenwater, Zoe Mickley. “Examples of flexible layouts with CSS3 media queries. ” Stunning CSS3: 320. Dec 15, 2010. ISBN 978-0-321-722133.
    [16] W3Cschools, “CSS3 @media Rule”, http://www.w3schools.com/cssref/css3_pr_mediaquery.asp, 2016, [access time: 2, 2016]
    [17] “Media Query Snippets” , http://nmsdvid.com/snippets/, 2016, [access time: 3, 2016]
    [18] Tor-Morten Grønli, Gheorghita Ghinea, Muhammad Younas, “Context-aware and automatic configuration of mobile devices in cloud-enabled ubiquitous computing.”,2013
    [19] Seok Jong Yu, “The dynamic competitive recommendation algorithm in social network services.”, Sookmyung Women’s University,2012
    [20] Ago Luberg, Karin Schoefegger, Priit Järv, Tanel Tammet. ,“Context-aware and Multilingual Information Extraction for a Tourist Recommender System.”,2011

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