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Detection of Auction Fraud in Commercial Sites
6 references·0 citing·24 second hop
Detection of Auction Fraud in Commercial Sites (2019)Detection of Auction Fraud in Commercial …Online Detection of Shill Bidding Fraud Based on Machine Learning Techniques (2018)Online Detection of Shill Bid…An empirical comparison of techniques for the class imbalance problem in churn prediction (2017)An empirical comparison of te…A case study on attitude towards online auction use applying quantile regression analysis (2017)A case study on attitude towa…A dynamic stage-based fraud monitoring framework of multiple live auctions (2016)A dynamic stage-based fraud m…A Real-Time Self-Adaptive Classifier for Identifying Suspicious Bidders in Online Auctions (2012)A Real-Time Self-Adaptive Cla…Combating online in-auction fraud: Clues, techniques and challenges (2009)Combating online in-auction f…COUNTERSPECULATION, AUCTIONS, AND COMPETITIVE SEALED TENDERS (1961)A Theory of Auctions and Competitive Bidding (1982)The Digitization of Word of Mouth: Promise and Challenges of Online Feedback Mechanisms (2003)REASONING UNDER UNCERTAINTY FOR SHILL DETECTION IN ONLINE AUCTIONS USING DEMPSTER–SHAFER THEORY (2010)Price comparison: A reliable approach to identifying shill bidding in online auctions? (2011)User Heterogeneity and its Impact on Electronic Auction Market Design: An Empirical Exploration1 (2004)How Valuable Is a Good Reputation? A Sample Selection Model of Internet Auctions (2005)Reputation in Auctions: Theory, and Evidence from e Bay (2006)Incremental learning framework for real‐world fraud detection environment (2021)Intention, Plans, and Practical Reason. (1991)PENNIES FROM EBAY: THE DETERMINANTS OF PRICE IN ONLINE AUCTIONS* (2007)Detection of financial statement fraud and feature selection using data mining techniques (2010)Understanding the Customer Base of Service Providers: An Examination of the Differences between Switchers and Stayers (2000)Defection Detection: Measuring and Understanding the Predictive Accuracy of Customer Churn Models (2006)Handling class imbalance in customer churn prediction (2008)A Churn Prediction Model Using Random Forest: Analysis of Machine Learning Techniques for Churn Prediction and Factor Identification in Telecom Sector (2019)Explaining and predicting employees’ attrition: a machine learning approach (2020)A Review on Machine Learning Methods for Customer Churn Prediction and Recommendations for Business Practitioners (2024)Evaluating Structural Equation Models with Unobservable Variables and Measurement Error (1981)Perceived Usefulness, Perceived Ease of Use, and User Acceptance of Information Technology (1989)User Acceptance of Information Technology: Toward A Unified View1 (2003)A new reality: Exploring continuance intention to use mobile augmented reality for entertainment purposes (2021)Reputation systems (2000)Detecting Shill Bidding in Online English Auctions (2009)
this work references cited by second hop

References

  • 2017An empirical comparison of techniques for the class imbalance problem in churn prediction161 cited
  • 2009Combating online in-auction fraud: Clues, techniques and challenges69 cited
  • 2012A Real-Time Self-Adaptive Classifier for Identifying Suspicious Bidders in Online Auctions36 cited
  • 2018Online Detection of Shill Bidding Fraud Based on Machine Learning Techniques25 cited
  • 2016A dynamic stage-based fraud monitoring framework of multiple live auctions24 cited
  • 2017A case study on attitude towards online auction use applying quantile regression analysis5 cited

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