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| Title: | Facebook社群人脈網絡與粉絲頁推薦之研究
The Study of Recommendation on Social Connections and Fan Pages on Facebook |
| Authors: | 曾子洋
Tseng, Tzu Yang |
| Contributors: | 楊建民
Yang, Jiann Min
曾子洋
Tseng, Tzu Yang |
| Keywords: | 社群網站
Facebook
Kmeans
粉絲頁
推薦
social website
Facebook
Kmeans
fan page
recommendation |
| Date: | 2012 |
| Issue Date: | 2013-09-02 16:00:57 (UTC+8) |
| Abstract: | Facebook自從在台灣推出以來，已有超過一千三百萬的使用者帳號，是最熱門的社群網站，其中蘊含了龐大的使用者資料。從使用者學歷、工作經歷和喜歡的粉絲頁中可以一定程度上地判斷出使用者的背景與喜好，若能利用分析過的資訊將使用者分群，以供交友或導向到可能喜歡的粉絲頁，就能開發潛在客戶進而掌握商機。
本研究旨在完成一個線上系統，透過Facebook上可供擷取個人的資料：學歷、工作經歷以及喜歡的粉絲頁等資訊，針對這些量化過的資訊，經Kmeans將使用者分群分類，藉以作為協同過濾式推薦。目前實驗結果將有效個人資料4417筆進行分群，以使用者喜歡的粉絲頁比例(本研究整合成48種)加上工作經歷與學歷，最終分成10群，以作為交叉推薦之憑據和延伸研究。研究過程分實驗組與對照組，實驗組是本研究推薦的10筆粉絲頁，也就是使用者與所屬群集質心比例相差較多的粉絲頁類型；對照組則是選取使用者與母體中有較多比例差距的10筆，以證明本研究的推薦模型有效。
最後由使用者針對兩組推薦結果進行滿意度評分之比較，總共收回使用者回饋68筆，實驗組與對照組的平均推薦滿意度分數分別為0.5743、0.4268，對兩者作信心水準為95%的t檢定，結果為有充分證據支持實驗組大於對照組，可證明本研究對於推薦準確性的幫助，達成本研究目的。
由此實驗可以確定在Facebook上以使用者屬性為基礎的粉絲頁與人脈推薦是有意義與價值的，也說明真實數據能應用在社群網站的研究。希冀本研究的結果能帶動其他社群網站研究朝使用真實數據去分析佐證，讓社群網站的研究結果能更貼近使用者的真實行為。
Facebook is one of the most popular social websites in Taiwan, and it has over 13 million accounts with lots of user data. One can tell a user’s background and preference by his education, work experience, and preferred fan pages. If we direct the right user to the right fan pages by analyzing information and clustering users through recommendation or personal connections, we will be able to reach potential customers and to further business opportunities.
The goal of this study is to complete an online system to assume collaborative fan page recommendation. Base on users’ education degree, work experience and preferred fan pages, users’ background. Then use the Kmeans algorithm to cluster quantified personal information to recommend fan pages and social relationships. Currently, the result of the experiment shows 10 clusters, which contain 4417 users, and we use it as a foundation of crossing recommendation. To prove the effect of this study, we divide study into two groups, an experimental group and control group. The former one represents recommended top 10 fan pages that include the fan page types with highest difference of percentage between user’s attributes and cluster centroid; the latter one represents top 10 fan pages that include the fan page types with highest difference of percentage between users’ attributes and proportion respectively.
Finally, we use users score satisfaction for each group to compare. There are 68 pieces of feedback, and the average satisfaction scores of the experimental group and the control group are 0.5743 and 0.4268, respectively. On both a confidence level of 95% for t-test, the result shows there is more sufficient evidence to support the satisfaction of experimental group than the control group. We can prove accuracy for recommendation to achieve the goal in this study.
This experiment determines not only the fan page recommendation based on user attributes on Facebook is meaningful and valuable, but also shows real data can be used in social networking studies. We hope the results of this study can lead other social networking studies to analyze with real users’ data in order to make future study on social networking better reflect real users’ behavior. |
| Reference: | 中文文獻
[1] 2012 TOPCO 崇越論文大賞，社群網站使用動機及前置因素之研究-以Facebook 為例。
[2] 林斐清(2012)，應用使用者之Facebook社會網絡關係建立協同過濾推薦系統，屏東科技大學資訊管理系所，碩士論文。
[3] 李瑞元、蕭丞彥(2012)，Facebook使用者的知覺社群價值之研究，實踐大學資訊科技與管理學系碩士班，碩士論文，p. 18-22。
[4] 范姜雅、李晏華、區國良(2012)， Facebook互動內容為基礎之主動式推薦系統，國立新竹教育大學數位學習科技研究所，2012 International Conference on Digital Content. Tainan, Taiwan。
[5] 張筱可(2012)，約略集為基礎的關聯法則於網路消費者推薦機制與改變行為之研究，淡江大學管理科學學系，碩士論文。
[6] 楊建民、呂春美(2011)，建構人脈社會網絡人才推薦系統之研究-以某國立大學EMBA人才庫為例，國立政治大學資訊管理學系，碩士論文。
[7] 楊建民、蔡承翰(2010)，以語意網建構人才推薦與信任推論機制之研究-以某國立大學EMBA 人才庫為例，國立政治大學資訊管理學系，碩士論文。
[8] 萬榮水、梁瑞文(2007)，虛擬社群之衡量及其影響因素之探討- -以網路書店為例，南華大學出版事業管理研究所，碩士論文。
[9] 蔡至欣、賴玲玲(2011)，虛擬社群的資訊分享行為，Information Sharing of the Virtual Community，圖書資訊學刊，第9卷第1期。
英文文獻
[10] Al-Daoud, M.B. and Roberts, S.A. (1996). New Methods for the Initialization of Clusters. Pattern Recognition Letters, vol. 17, p. 451-455.
[11] Bradley, P.S., Fayyad, U. and Reina, C. (1998). Scaling Clustering Algorithms to Large Databases. In Proceedings of the 4th International Conference on Knowledge Discovery and Data Mining, Menlo Park, Calif., p. 9-15.
[12] Burt, R. S. (1977). The cognitive value of social capital. Administrative Science Quarterly, 42, 339-365.
[13] Dwyer, C., Hiltz, S. R., & Passerini, K. (2007). Trust and privacy concern within social networking sites: A comparison of Facebook and MySpace. Proceedings of AMCIS.
[14] Katz, M. L. and Shapiro, C. (1985), Network externalities, competition, and compatibility, The American Economic Review, Vol. 75, No. 3, p. 424-440.
[15] Kleinberg, J. (2000). The small-world phenomenon: An algorithmic perspective. Annual ACM symposium on theory of computing, Vol. 32, p. 163–170.
[16] Kreijns, K., Kirschner, P. A., Jochems, W. and Buuren, H. V. (2007), “Measuringperceived sociability of computer-supported collaborative learning environments,” Computer & Education, Vol. 49, No. 2, p. 176-192.
[17] Kujala, S. & Mattila, V. V. K. (2007). Value of Information Systems and Products: Understanding the Users’ Perspective and Values. Journal of Information Technology Theory and Application, p. 23-29.
[18] Rheingold, H. (1993). The virtual community: Homesteading on the electronic frontier. Basic Books.
[19] Rheingold, H. (2000). The Virtual Community: Homesteading On the ElectronicFrontier. Cambridge, Mass.: MIT Press.
[20] Schafer, J. B., Konstan, J., & Riedi, J. (1999). Recommender systems in e-commerce. Proceedings of the 1st ACM conference on Electronic commerce, p. 158–166.
[21] Zeithaml, V.A. (1988). Consumer Perceptions of Price, Quality, and Value: A Means-End Model and Synthesis of Evidence., Journal of Marketing (52:3), p. 2-22.
網路資料
[22] Blog Read/Write，http://readwrite.com/2007/01/16/recommendation_engines。
[23] EMBA智庫，http://wiki.mbalib.com/zh-tw/網路廣告效果評估。
[24] Facebook也要開商業平台了要賣東西囉，http://blog.xuite.net/gina0987551199/blog/44628809。
[25] Facebook API document， http://developers.facebook.com/docs/reference/api/。
[26] Facebook FQL， http://developers.Facebook.com/docs/reference/fql/。
[27] Google Web Search API， https://developers.google.com/web-search/docs/。
[28] InsideFacebook， http://www.insidefacebook.com/。
[29] Ticketmaster ticketing website fan page on Facebook，http://www.facebook.com/Ticketmaster。
[30] 台灣大學電子計算機中心電子報， http://www.cc.ntu.edu.tw/chinese/epaper/0016/20110320_1605.html。
[31] 數位時代，台灣社群網站回訪率，Facebook最高，http://www.bnext.com.tw/article/view/cid/128/id/19746。 |
| Description: | 碩士
國立政治大學
資訊管理研究所
100356016
101 |
| Source URI: | http://thesis.lib.nccu.edu.tw/record/#G0100356016 |
| Data Type: | thesis |
| Appears in Collections: | [資訊管理學系] 學位論文
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