跳到主要內容

臺灣博碩士論文加值系統

(3.235.185.78) 您好!臺灣時間:2021/07/27 18:24
字體大小: 字級放大   字級縮小   預設字形  
回查詢結果 :::

詳目顯示

我願授權國圖
: 
twitterline
研究生:黃泓翔
論文名稱:基於社會網路的拍賣平台專家推薦系統之研究
指導教授:楊建民楊建民引用關係
學位類別:碩士
校院名稱:國立政治大學
系所名稱:資訊管理研究所
學門:電算機學門
學類:電算機一般學類
論文種類:學術論文
畢業學年度:97
語文別:中文
論文頁數:65
中文關鍵詞:社會網路推薦系統協同過濾專家推薦
外文關鍵詞:Social NetworkRecommendation SystemCollaborative FilteringExpert Recommendation
相關次數:
  • 被引用被引用:4
  • 點閱點閱:256
  • 評分評分:
  • 下載下載:0
  • 收藏至我的研究室書目清單書目收藏:0
在人們的日常生活中,推薦是很普遍的一種社會行為,它使人們不必親自去體驗所有的事物,可透過別人的經驗來得知一件事情或商品的好或壞。隨著科技的快速發展與網際網路的普及,電子商務已逐漸的融入社會,成為人類生活中不可或缺的一部分。然而在網路上過量的資訊,使得個人在資訊的使用與搜尋上面臨極大的挑戰,更加刺激了對於推薦資訊的需求,因此許多推薦技術相繼提出,推薦系統也應運而生,不僅使得推薦的範圍擴大了,推薦的型態也更為豐富多元;同時,在近年電子商務的發展中,對於個人化與顧客導向服務的愈益重視,使得推薦系統逐漸成為一種必要的線上服務。
在眾多的推薦技術之中,協同過濾推薦方法是最成功且最常被採用的推薦技術之一,許多台灣的拍賣平台上也都有採用類似概念的推薦系統,像是Yahoo!拍賣、露天拍賣上的評價機制均屬此類。然而,現行的拍賣評價機制都沒有採用社會網路的技術,本研究希望透過協同過濾與社會網路的結合,讓評價機制更趨於完備。
本研究以台灣最大的拍賣網站Yahoo!為例,蒐集了44萬筆交易記錄,並以推薦網(ReferralWeb)系統的矩陣方法為基礎,找出人與商品的關係、商品與類別的關係、人與人的關係,建立起一個社會網路,讓使用者可查詢特定領域的專家,並與之交易。除此之外,也可直接詢問專家關於商品的資訊或購買技巧。透過這樣的機制,希望能降低消費者在購買商品時所產生的交易糾紛,讓人們在網路上的購物體驗能變得更好。
Nowadays, recommendation is a common social behavior between people. People can evaluate things or commodities from others’ experience and opinions instead of their own experiences. Along with the development of technology and Internet today, E-commerce has become an indispensable part of human life. However, due to the overloaded information, people face a fantastic challenge when accessing and searching on the Internet. Therefore, many methods of recommendation were proposed, and systems of recommendation are to come with the tide of fashion. In addition, the development of E-commerce emphasized on personalization and customer-oriented services more in recent years, which make recommendation system becomes a necessary on-line service gradually.
Collaborative Filtering is the most successful and adopted one in numerous recommendation methods. There are many auction platforms in Taiwan also use recommendation systems, such like "Yahoo Auction", "Ruten Auction", etc. However, the previous mentioned recommendation mechanisms haven’t used Social Network technology; this study will propose an recommendation system which combines Collaborative Filtering and Social Network technology.
This research collects 440,000 transaction data from the Yahoo auction platform, which is the biggest auction website in Taiwan. Based on the matrix method of ReferralWeb system(Shah, 1997), this research would like to build up the matrix of relationships between Person-Commodity, Commodity-Category, and Person-Person. Based on the three matrixes, finally builds up a Social Network. In the Social Network, users can enquire experts refer to the specific category of commodity, and then refer to the shops which the experts like or directly ask them the commodity information and purchase skill. Relying on the mechanism proposed by this research, our goals are to reduce the transaction disputes arising from consumers purchase commodities, and to let people have better experiences in on-line shopping.
謝辭 I
摘要 II
圖目錄 VII
表目錄 VIII
第一章 緒論 1
第一節 研究背景與動機 1
第二節 研究目的 3
第三節 研究架構 4
第二章 文獻探討 5
第一節 推薦系統 5
一、推薦系統應用架構 6
二、推薦系統介面的呈現 9
三、推薦系統的分類 10
第二節 資訊過濾 11
ㄧ、內容基礎過濾 11
二、協同過濾 12
三、經濟式過濾 13
第三節 協同過濾 15
一、協同過濾的定義 15
二、協同過濾的運作機制 15
三、協同過濾的相關系統 17
四、協同過濾的優缺點 18
第四節 社會網路 21
一、社會網路分析 21
二、社會網路分析的單元 21
三、社會網路分析的種類 24
四、推薦網(ReferralWeb) 26
第五節 小結 26
第三章 研究方法 27
第一節 研究流程與系統架構 27
一、推薦網(ReferralWeb)的系統流程 27
二、本研究的系統流程與架構 29
第二節 蒐集使用者交易與評價記錄 31
一、買賣的人際關係 31
二、商品與類別的關係 31
三、資料蒐集方法 32
四、資料蒐集量 32
第三節 建立使用者社會關係網路 33
一、人與人的關係假設 33
二、人與人矩陣 33
三、關係強度的定義 34
四、人與人關係示意圖 34
第四節 建立各類別的專家排行榜 36
一、人與類別的關係假設 36
二、人與類別矩陣 36
三、專家強度的定義 37
四、類別專家排行榜 37
第五節 研究限制 38
一、蒐集資料的先天限制 38
二、實際蒐集資料所碰到的瓶頸 38
第四章 系統建置與研究成果 39
第一節 系統架構 39
一、資料蒐集器 39
二、分析引擎 40
三、資料庫 40
四、使用者介面 40
第二節 資料的敘述統計 41
一、熱門類別排行榜 41
二、熱門類別的專家排行 41
第三節 人與人的關係 43
一、情境模擬 43
二、人與人關係圖 43
三、人與人的關係表 45
四、小結 45
第四節 關係鏈與類別的專家排行 46
一、情境模擬 46
二、人與專家之關係鏈 46
三、類別的專家排行 47
四、個人的興趣排行 48
五、小結 48
第五節 負評機制修正專家排行榜 49
第六節 使用者輪廓 50
第七節 小結 50
第五章 結論、建議與未來展望 51
第一節 結論與建議 51
第二節 未來研究方向 52
一、加速資料蒐集的速度 52
二、自行定義“無類別商品”的類別 52
三、全自動化的呈現與分析 53
參考文獻 54
1. 王珮華(2007),「全民瘋網拍 奇摩、露天持續成長」,http://forum.ruten.com.tw/replylist.php?article=1650602
2. Borgatti, S.P. (1998), “What Is Social Network Analysis?”, http://www.analytictech.com/networks/whatis.htm
3. Belkin, N.J. and Croft, W.B. (1992), “Information Filtering and Information Retrieval: Two Sides of the same coin?,” Communications of the ACM, Vol.35, Iss.12, pp.29-38.
4. Breese, J., Heckerman, D. and Kadie, C. (1998), “Empirical Analysis of Predictive Algorithms for Collaborative Filtering,” Proceedings of the 14th Conference on Uncertainty in Artificial Intelligence, pp.43-52.
5. Castells, M. (2000/1996), The Rise of the Network Society (2nd Edition). Oxford: Blackwell. 夏鑄九、王志弘譯(2000),網路社會之崛起。台北:唐山書局。
6. Cohen, J. (1992), “Special Issue on Information Filtering,” Communication of the ACM, Vol.35, Iss.12.
7. Constant, D. and Sproull, L. and Kiesler, S. (1996), “The Kindness of Strangers: The Usefulness of Electronic Weak Ties for Technical Advice,” Organization Science, Vol.7, Iss.2, pp.119-135.
8. Dhillon, N. (1995), “Achieving Effective Personalization and Customization Using Collaborative Filtering,” from http://home1.gte.net/dhillos/cf.
9. Duck, S. (1998/1986), Human Relationships (3rd Edition). London: Sage.
10. Dunbar, R.I.M. (1992), “Neocortex size as a constraint on group size in primates,” Journal of Human Evolution, 20, pp.469-493.
11. Freeman, L.C. (2004), The Development of Social Network Analysis: A Study in the Sociology of Science. Vancouver: Empirical Press.
12. Garton, L., Haythornthwaite, C. and Wellman, B. (1997), “Studying Online Social Networks,” Journal of Computer-Medicated Communication, Vol.3, Iss.1.
13. Goldberg, D., Nichols, D., Oki, B.M. and Terry, D. (1992), “Using Collaborative Filtering to Weave an Information Tapestry,” Communication of the ACM, Vol.35, Iss.12, pp.61-70.
14. Granovetter, M.S. (1973), “The Strength of Weak Ties,” The American Journal of Sociology, Vol.78, No.6, pp.1360-1380.
15. Hanneman, R. and Riddle M. (2005), Introduction to Social Network Methods. Riverside, CA: University of California, Riverside (published in digital form at http://faculty.ucr.edu/~hanneman/)
16. Hill, R. and Dunbar, R. (2003), “Social Network Size in Humans,” Human Nature, Vol.14, No.1, pp.53-72.
17. Hill, W., Stead, L., Rosenstein, M. and Furnas, G. (1995), “Recommending and Evaluating Choices in A Virtual Community of Use,” Proceedings of the SIGCHI conference on Human factors in computing systems, pp.194-201.
18. Herlocker, J., Konstan, J., and Riedl, J. (2000), “Explaining Collaborative Filtering Recommendations,” Proceedings of the 2000 ACM conference on Computer supported cooperative work, pp.241-250.
19. Kautz, H., Selman, B. and Shah, M. (1997), “Referral Web: Combining Social Networks and Collaborative Filtering,” Communications of the ACM, Vol.40, Iss.3, pp.63-65.
20. Kautz, H. and Selman, B. (1998), “Creating Models of Real-World Communities with ReferralWeb,” Working notes of the Workshop on Recommender Systems, pp.58-59.
21. Kazdan, A.E. (2000), Encyclopedia of Psychology. New York: Oxford University Press.
22. Konstan, J., Miller, B.N., Maltz, D., Herlocker, J., Gordon, L. R. and Riedl J. (1997), “GroupLens: Applying Collaborative Filtering to Usenet News,” Communication of the ACM, Vol.40, Iss.3, pp.77-87.
23. Nichols, D.M. (1997), “Implicit Rating and Filtering,” Proceedings of 5th DELOS Workshop on Filtering and Collaborative Filtering, pp.28-33.
24. Oard, D.W. and Marchionini, G. (1996), A Conceptual Framework for Text Filtering. University of Maryland, College Park.
25. Palme, J. (1997), “Choice in the Implementation of Rating,” Proceedings of 5th DELOS Workshop on Filtering and Collaborative Filtering, pp.34-55.
26. Pattison, P. (1993), Algebraic Models for Social Networks. Cambridge: Cambridge University Press.
27. Perlman, D. and Fehr, B. (1987), The Development of Intimate Relationships. CA: Sage Publications.
28. Pine II, B.J., Peppers, D. and Rogers, M. (1995), “Do you want to keep your customers forever?,” Harvard Business School Review, Vol.73, Iss.2, pp.103-114.
29. Resnick, P., Iacovou, N., Sushak, M., Bergstrom, P. and Riedl, J. (1994), “GroupLens: An Open Architecture for Collaborative Filtering of Netnews,” Proceedings of the 1994 ACM conference on Computer supported cooperative work, pp.175-186.
30. Resnick, P. and Varian, H.R. (1997), “Recommender systems,” Communications of the ACM, Vol.40, Iss.3, pp.56-58.
31. Ricci, F. (2002), “Travel recommender systems,” IEEE Intelligent Systems, Vol.17, Iss.6, pp.55.
32. Sarwar, B., Konstan, J., Borchers, A., Herlocker, J., Miller, B., and Riedl, J. (1998), “Using Filtering Agents to Improve Prediction Quality in the GroupLens Research Collaborative Filtering System,” Proceedings of the 1998 ACM conference on Computer supported cooperative work, pp.345-354.
33. Shah, M.A. (1997), ReferralWeb: A resource location system guided by personal relations, Master's thesis, M.I.T.
34. Schafer, J.B., Konstan, J. and Riedl, J. (2001), “E-Commerce Recommendation Applications,” Journal of Data Mining and Knowledge Discovery, Vol.5, No.1-2, pp.115-153.
35. Schafer, J.B., Konstan, J. and Riedl, J. (1999), “Recommender Systems in E-Commerce,” Proceedings of the 1st ACM Conference on Electronic Commerce, pp.158-166.
36. Scott, J. (2000), Social Network Analysis: A Handbook (7th Edition). London: Sage Publications.
37. Shardanand, U. and Maes, P. (1995), “Social Information Filtering: Algorithms for Automating "Word of Mouth",” Proceedings of CHI'95 Conference on Human Factors in Computing Systems, Vol.1, pp.210-217.
38. Terry, D.B. (1993), “A tour through Tapestry,” Proceedings of the conference on Organizational computing systems, pp.21-30.
39. Wasserman, S. and Faust, K. (1994), Social Network Analysis: Methods and Applications. New York: Cambridge University Press.
40. Wellman, B., Carrington, P. and Hall, A. (1988), “Networks as Personal Communities,” In B. Wellman and S.D. Berkowitz (ed.), Social Structures: A Network Approach (pp.130-184). Cambridge: Cambridge University Press.
41. Wellman, B. (1992), “Which Types of Ties and Networks Give What Kinds of Social Support?,” Advances in Group Processes, Vol.9, pp.207-235.
42. Wellman, B. (1996), “For a Social Network Analysis of Computer Networks: A Sociological Perspective on Collaborative Work and Virtual Community,” Proceedings of the 1996 ACM SIGCPR/SIGMIS conference on Computer personnel research, pp.1-11.
43. Wiseman, J.P. (1986), “Friendship: Bonds and Binds in Voluntary Relationship,” Journal of Social and Personal Relationships, Vol.3, No.2, pp.191-211.
44. 陳俊彰(2000),「從網頁中發掘教師知識分佈圖」,中山大學資訊管理學系研究所碩士論文。
45. 張瀚仁(2000),「個人化技術對虛擬社群發展之影響」,政治大學資訊管理學系研究所碩士論文。
46. 鄭秀華、廖婉菁、吳肇銘(2003),「線上商品推薦系統之研究—協同過濾機制之應用」,2003電子商務與數位生活研討會。
47. 馮文正(2001),「合作式網站推薦系統」,交通大學資訊科學系研究所碩士論文。
48. 羅健銘(2001),「協同過濾於網站推薦之研究」,台北科技大學商業自動化與管理研究所碩士論文。
49. 廖婉菁(2002),「應用協同過濾機制於商品推薦之研究─以手機網站為例」,中原大學資訊管理學系研究所碩士論文。
QRCODE
 
 
 
 
 
                                                                                                                                                                                                                                                                                                                                                                                                               
第一頁 上一頁 下一頁 最後一頁 top
無相關期刊