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| Title: | 基於社會網路的拍賣平台專家推薦系統之研究 |
| Authors: | 黃泓翔 |
| Contributors: | 楊建民
黃泓翔 |
| Keywords: | 社會網路
推薦系統
協同過濾
專家推薦
Social Network
Recommendation System
Collaborative Filtering
Expert Recommendation |
| Date: | 2008 |
| Issue Date: | 2010-04-08 16:29:07 (UTC+8) |
| Abstract: | 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. |
| Reference: | 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。 |
| Description: | 碩士
國立政治大學
資訊管理研究所
95356018
97 |
| Source URI: | http://thesis.lib.nccu.edu.tw/record/#G0095356018 |
| Data Type: | thesis |
| Appears in Collections: | [資訊管理學系] 學位論文
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