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研究生:鍾孟潔
研究生(外文):Meng-Chieh Chung
論文名稱:線上團購模式預測成團之研究
論文名稱(外文):An Effective Method for Predicting the Success of Group Buying Auctions
指導教授:陳建錦陳建錦引用關係
口試委員:陳孟彰盧信銘蔡銘峰
口試日期:2013-07-24
學位類別:碩士
校院名稱:國立臺灣大學
系所名稱:資訊管理學研究所
學門:電算機學門
學類:電算機一般學類
論文種類:學術論文
論文出版年:2013
畢業學年度:102
語文別:英文
論文頁數:32
中文關鍵詞:商業智慧風險分析分類
外文關鍵詞:Business IntelligenceRisk AnalysisClassification
相關次數:
  • 被引用被引用:1
  • 點閱點閱:361
  • 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:1
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)。

Online 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.

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


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