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  1. NTU Theses and Dissertations Repository
  2. 管理學院
  3. 資訊管理學系
請用此 Handle URI 來引用此文件: http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/58702
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dc.contributor.advisor陳建錦
dc.contributor.authorMeng-Chieh Chungen
dc.contributor.author鍾孟潔zh_TW
dc.date.accessioned2021-06-16T08:26:29Z-
dc.date.available2016-01-27
dc.date.copyright2014-01-27
dc.date.issued2013
dc.date.submitted2014-01-20
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[2] C.-C. Chang, C.-J. Lin, LIBSVM: A library for support vector machines, ACM Trans. Intell. Syst. Technol., 2(3) (2011) 1-27.
[3] C.-P. Chen, Online Group Buying Behavior in CC2B e-Commerce: Understanding Consumer Motivations, Journal of Internet Commerce, 11(3) (2012) 254-270.
[4] J. Chen, X. Chen, R.J. Kauffman, X. Song, Should we collude? Analyzing the benefits of bidder cooperation in online group-buying auctions, Electronic Commerce Research and Applications, 8(4) (2009) 191-202.
[5] J. Chen, X. Chen, X. Song, Comparison of the group-buying auction and the fixed pricing mechanism, Decision Support Systems, 43(2) (2007) 445-459.
[6] H. Estelami, D.R. Lehmann, A.C. Holden, Macro-economic determinants of consumer price knowledge: A meta-analysis of four decades of research, International Journal of Research in Marketing, 18(4) (2001) 341-355.
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[9] X. Guobiao, Z. Jie, L. Qiang, X. Suxiu, Influencing factors of consumer intention towards web group buying, in: Industrial Engineering and Engineering Management (IEEM), 2011 IEEE International Conference on, (2011), pp. 1397-1401.
[10] T. Hennig-Thurau, K.P. Gwinner, G. Walsh, D.D. Gremler, Electronic word-of-mouth via consumer-opinion platforms: what motivates consumers to articulate themselves on the internet?, Journal of interactive marketing, 18(1) (2004) 38-52.
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[13] R.J. Kauffman, H.-C. Lai, C.-T. Ho, Incentive mechanisms, fairness and participation in online group-buying auctions, Electronic Commerce Research and Applications, 9(3) (2010) 249-262.
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[16] R.J. Kauffman, B. Wang, Bid together, buy together: On the efficacy of group-buying business models in Internet-based selling, in, (CRC Press, 2002), pp. 99-137.
[17] Y.A. Kim, J. Srivastava, Impact of social influence in e-commerce decision making, in: Proceedings of the ninth international conference on Electronic commerce, (ACM, Minneapolis, MN, USA, 2007), pp. 293-302.
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dc.identifier.urihttp://tdr.lib.ntu.edu.tw/jspui/handle/123456789/58702-
dc.description.abstract在線上團購中,對買家來說,團購隱含著風險與不確定性。由於買家無法從失敗的團購(流團)得利,因此在決定要加入團購之前,他們會考慮很多因素。過去的文獻裡,許多學者針對這些影響買家決策的因素進行量化研究。學者們探討出各種影響團購決策的因素,儘管如此,只有少部分的研究企圖去量化它們,或者使用它們為團購進行成功(成團)或失敗(流團)的預測。本論文研究此問題,並提出一個能有效預測團購結果(是否成團)的方法。本研究將團購成功與否的預測看作是分類問題,並將前人量化研究得出的相關因素,彙整成五個維度與十二項因子,進一步使用這些因子去預測團購結果。同時使用現實世界團購網的真實資料進行實驗,以證明本方法是有效可行的。而實驗結果顯示,本論文提出的方法在預測的準確率(precision)、召回率(recall)和F值(F1 score)方面皆優於社交傳播模型(social propagation model)。zh_TW
dc.description.abstractOnline group buying involves risks and uncertainties for buyers. Because buyers would not benefit from a failed group buying auction, they usually consider several factors before deciding to join an auction. The factors that affect buyers’ decisions have been qualitatively investigated in the literature. However, few studies have attempted to quantitate the factors, or utilize them to predict an auction’s success. In this paper, we propose an effective method for predicting the success of a group buying auction. We model success prediction as a classification problem, and utilize five dimensions and twelve features derived from previous qualitative research to predict the success of group buying auctions. Experiments based on a real world group buying system demonstrate the efficacy of the proposed method. Moreover, the method outperforms a social propagation model in terms of the prediction precision rate, recall rate, and F1 score.en
dc.description.provenanceMade available in DSpace on 2021-06-16T08:26:29Z (GMT). No. of bitstreams: 1
ntu-102-R00725038-1.pdf: 694433 bytes, checksum: e2ef2d9faf9fa1df33e284353ef9d9b7 (MD5)
Previous issue date: 2013
en
dc.description.tableofcontents口試委員會審定書 i
誌謝 ii
中文摘要 iii
ABSTRACT iv
LIST OF CONTENTS v
LIST OF FIGURES vi
LIST OF TABLES vii
1. Introduction 1
2. Related Work 5
2.1 Online Group Buying Mechanisms and Properties 5
2.2 Factors that Influence Online Group Buying Decisions 7
3. MethodologyEquation Chapter (Next) Section 1 9
3.1 Problem Definition 9
3.2 Auction Feature Extraction 10
3.3 Predicting the Success of Auction 16
4. Experiments 18
4.1 Dataset and Performance Metrics 18
4.2 Auction Dimension Evaluations 21
4.3 Comparison with an Order Quantity Prediction Method 27
5. Conclusion 30
REFERENCE 31
dc.language.isoen
dc.subject風險分析zh_TW
dc.subject商業智慧zh_TW
dc.subject分類zh_TW
dc.subjectBusiness Intelligenceen
dc.subjectRisk Analysisen
dc.subjectClassificationen
dc.title線上團購模式預測成團之研究zh_TW
dc.titleAn Effective Method for Predicting the Success of Group Buying Auctionsen
dc.typeThesis
dc.date.schoolyear102-1
dc.description.degree碩士
dc.contributor.oralexamcommittee陳孟彰,盧信銘,蔡銘峰
dc.subject.keyword商業智慧,風險分析,分類,zh_TW
dc.subject.keywordBusiness Intelligence,Risk Analysis,Classification,en
dc.relation.page32
dc.rights.note有償授權
dc.date.accepted2014-01-21
dc.contributor.author-college管理學院zh_TW
dc.contributor.author-dept資訊管理學研究所zh_TW
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