Please use this identifier to cite or link to this item: https://ah.nccu.edu.tw/handle/140.119/69493


Title: 建置結合社群互動圈的個人化餐廳推薦系統
Design and Implementation of a Personalized Restaurant Recommendation System
Authors: 黃資雅
Contributors: 張寶芳
蔡子傑

Chang, Pao Fang
Tsai, Tzu Chieh

黃資雅
Keywords: 推薦系統
社群互動圈
情境感知
Recommender System
Social Network
Context Awareness
Date: 2013
Issue Date: 2014-09-01 13:56:05 (UTC+8)
Abstract:   選擇到哪家餐廳用餐的問題,不論旅遊或家居都經常會遇到。大多數的人會先上網,尋找符合自己喜好且評價好的美食。然而網際網路發達,在人人都可上網分享的情況下,造成資訊氾濫超載。使得使用者上網瀏覽資料時,很容易找到不切合需求的資訊。解決此資訊超載的方法之一是餐廳推薦系統。儘管目前有很多的推薦應用程式或是分享平台,諸如TripAdvisor、iPeen愛評網、foursquare…等等,資料豐富但卻沒有針對個人偏好做推薦。
  本研究有鑒於許多人在品嘗美食之前,會先拍照並在Facebook或Instagram打卡做紀錄、分享給朋友,打卡的次數可能意味著此餐廳的熱門度。且使用者選擇的美食類型偏好也可能受到聚餐目的的影響。因此開發出一款結合社群互動圈以及考量用餐情境的餐廳推薦系統。此系統先利用使用者所選擇的聚餐場合、價位、餐廳類型、熱門商圈等元素篩選出合適的餐廳,再利用Facebook打卡資料取得與使用者偏好相似的好友,依據好友的相似度推算出使用者對餐廳的喜好程度,推薦符合使用者興趣及需求的餐廳,協助使用者能夠更容易地找到自己所喜好的店家。
  本研究的實作系統,經過評估測試,結果發現結合社群互動圈及考量用餐情境的個人化推薦能讓使用者更容易找到自己所喜好的餐廳,而在推薦內容中顯示好友對餐廳的評論,更有效的幫助使用者作決策。未來本推薦系統所使用之結合情境元素所設計的模式亦可應用至其他領域的推薦平台,如旅遊景點推薦或旅遊住宿推薦。
  Most people face the issue of deciding which restaurant to eat. Searching through the Internet is the first step that people usually do. However the rapid growth of information has overloaded the Internet users, it makes difficult to find the most appropriate information for decision-making. Certainly there are several restaurant recommender systems have been developed to solve the problem, such as TripAdvisor, iPeen, foursquare, etc; but few systems provide personalized and context-based recommendations.
  The research intends to develop a restaurant recommender system that considers the factors of social network and context. Nowadays, when people eat, they like to take a picture and check in on Facebook or Instagram to share with friends, the numbers of check-in for a restaurant may mean the restaurant’s popularity. In addition, the gathering purpose and personal preferences may also affect the users’ decisions. Therefore the recommender system first used the variables of eating criteria such as place, price, types of food, eating environments to filter restaurants. The system then got the user’s similar friends from check-in data of Facebook. Through calculating friends’ similarity and their preference of restaurants, the recommender system finds the most fitted ones for the user to choose from.
  The afterward system’s users testing data prove that this personalized and context-based recommendation system provides better information to help the user make their decisions. The same model can be replicated to other domain of recommender platforms.
Reference: 一、中文部分
1.吳照輝(2012)。社交媒體對網路消費者社交購物決策行為之影響,國立台北科技大學服務與科技管理所碩士論文。
2.吳濟聰(2012)。朋友圈餐廳推薦機制之研究,第八屆知識社群國際研討會,357-367。
3.林斐清(2012)。用使用者之Facebook社會網絡關係建立協同過濾推薦系統,國立屏東科技大學資訊管理研究所碩士論文。
4.林涵婷(2013)。訊息特性對網路口碑傳遞效果的影響─以非營利網路社群為例,2013第16屆科技整合管理研討會,1-15。
5.范姜雅藍(2012)。建構於Facebook上之餐飲商店推薦系統,國立新竹教育大學數位科技學習研究所碩士論文。
6.翁頌舜(2006)。整合情境資訊之多維度推薦環境,2006電子商務與數位生活研討會論文集。
7.郭羿呈(2012)。使用社交圖與情境感知之行動餐廳推薦系統,國立台灣大學工程科學及海洋工程研究所碩士論文。
8.張譽馨、顏昌明、鄭帆評、賴志偉(2012)。情境感知推薦系統架構─以餐廳推薦為例,第23屆國際資訊管理學術研討會。
9.張碩庭(2013)。基於社群網站之手持裝置個人化餐廳推薦系統,國立台灣科技大學資訊與運籌管理研究所碩士論文。
10.黃啟嘉(2009)。情境資訊對智慧型裝置上餐廳推薦系統的影響分析,國立台灣大學電機資訊學院資訊工程學研究所碩士論文。
11.蕭富峰(2012)。消費者行為。台灣:智勝文化。

二、英文部分
1.Burke, R. (2007). Hybrid Web Recommender Systems. Springer-Verlag Berlin, Heidelberg, 377-408.
2.Balabanović, M., & Shoham, Y. (1997). Fab: Content-Based, Collaborative Recommendation. Communications of the ACM, 40(3), 66-72.
3.Dey, A.K., & Abowd, G.D. (1999). Towards a Better Understanding of Context and Context-Awareness. HUC '99 Proceedings of the 1st international symposium on Handheld and Ubiquitous Computing, 304-307.
4.de Vriesa, L., Genslera, S., & Leeflang, P.S.H. (2012). Popularity of Brand Posts on Brand Fan Pages: An Investigation of the Effects of Social Media Marketing. Journal of Interactive Marketing, 26(2), 83-91.
5.Guy, I., & Carmel, D. (2011). Social Recommender Systems. WWW 2011-Tutorial, 283-284.
6.Herlocker, J.L., & Konstan, J.A. (2001). Content-Independent Task-Focused Recommendation. IEEE Internet Computing, 5(6), 40-47.
7.Lucas, B., & Carlson, J. (2012). Exploring Factors Affecting Mobile Social Media Interactions within Service Environments: A Theoretical Framework. ANZMAC 2012 Proceedings.
8.Li, Q., & Kim, B.M. (2003). Clustering Approach for Hybrid Recommender System. Proceeding WI '03 Proceedings of the 2003 IEEE/WIC International Conference on Web Intelligence, 33-38.
9.Pazzani, M.J., & Billsus, D. (2007). Content-Based Recommendation Systems. Springer-Verlag Berlin, Heidelberg, 325-341.
10.Schafer, J.B., Frankowski, D., Herlocker, J., & Sen, S. (2007). Collaborative Filtering Recommender Systems. Springer-Verlag Berlin, Heidelberg, 291-324.
11.Schilit, B.N., & Theimer, M.M. (1994). Disseminating Active Map Information to Mobile Hosts. IEEE Network, 8(5), 22-32.
12.Tiroshi, A., Kuflik, T., Kay, J., & Kummerfeld, B. (2011). Recommender Systems and the Social Web. UMAP'11 Proceedings of the 19th international conference on Advances in User Modeling, 60-70.
13.Wang, J., Erdelez, S., & Thome, J. (2011). Online Consumer Information Encountering Experience for Planned Purchase and Unplanned Purchase. iConference '11 Proceedings of the 2011 iConference, 794-795.

三、網路部分
1.創市際(2012)。2012年網路社群白皮書。取自 http://zh.scribd.com/doc/117223697/2012年網路社群白皮書
2.Alexa(2013)。取自 http://www.alexa.com/siteinfo/facebook.com
3.中華民國交通部觀光局(2013)。2012年國人旅遊狀況調查。取自 http://admin.taiwan.net.tw/upload/statistic/20130726/56bed5af-09ea-4f51-8f4c-c3ecacdb7ec4.doc
4.尼爾森(2010)。尼爾森:影響消費者購物決策 口碑行銷力量大。取自 http://tw.nielsen.com/news20100628.shtml
5.Facebook Graph API。取自
https://developers.facebook.com/
6.智庫‧百科。服務接觸。取自
http://wiki.mbalib.com/zh-tw/服务接触
7.iPeen愛評網。取自
http://www.ipeen.com.tw/
8.foursquare。取自
https://foursquare.com/
9.TripAdvisor。取自
http://www.tripadvisor.com.tw/
10.韓巢。取自
http://cn.konest.com/
11.Google Maps Javascript API v3。取自
https://developers.google.com/maps/documentation/javascript/
12.台灣的Facebook用戶。取自
http://www.slideshare.net/fullscreen/yuanping/facebook-36279879
Description: 碩士
國立政治大學
數位內容碩士學位學程
99462011
102
Source URI: http://thesis.lib.nccu.edu.tw/record/#G0099462011
Data Type: thesis
Appears in Collections:[數位內容碩士學位學程] 學位論文
[數位內容碩士學位學程] 學位論文

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