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Reputation Systems

Reputation Systems. Guest Lecture Paul Resnick Associate Professor Univ. of Michigan School of Information presnick@umich.edu. Learning Objectives. Understand What a reputation system is Theory about when and why it should work Open research questions Participate in design

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Reputation Systems

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  1. Reputation Systems Guest Lecture Paul Resnick Associate Professor Univ. of Michigan School of Information presnick@umich.edu

  2. Learning Objectives • Understand • What a reputation system is • Theory about when and why it should work • Open research questions • Participate in design • Recognize situations when it might be helpful • Raise some of the difficult design challenges

  3. Outline • What is a reputation system? • Theory: when/why they should work • Empirical results • Design space • Case study: NPAssist recommender

  4. Definition • A Reputation System… • Collects • Distributes • Aggregates • …information about behavior

  5. Examples • BBB • Bizrate • eBay • Expertise sites • Epinions “top reviewers” • Slashdot karma system

  6. Why Reputation Systems • Interacting with strangers • Sellers (Exchange Partners) Vary • Skill • Effort • Ethics

  7. Other Trust-Inducing Mechanisms in E-commerce • Insurance • Escrow • Fraud Prosecution

  8. How Reputation Systems Should Work • Information • Incentive • Self-selection

  9. Some Issues • Anonymity • Name changes • Name trades • Lending reputations • Eliciting evaluation • Honesty of evaluations

  10. Anonymity Analysis

  11. 1L Pseudonyms • Third-party issues pseudonyms • No cost • Not replaceable • Reveal name to third party • Don’t reveal mapping of name to pseudonym

  12. Empirical Results: eBay • Feedback is provided • It’s almost all positive • Reputations are informative • Reputation benefits • Effect on probability of sale • Effect on price

  13. Provision of Feedback • Negatives: paid but did not receive; seller cancelled; not as advertised; communication • Neutrals: slow shipping, not as advertised, communication

  14. Feedback Profiles of Buyers and Sellers

  15. Predicting Problematic Transactions • Logistic Regressionf(0,0) = 1.91% f(100,0) = .18% f(100,3) = .53% N = 36233 Beginning Block Number 0. Initial Log Likelihood Function -2 Log Likelihood 2194.3468 -2 Log Likelihood 2075.420 Dependent Variable.. NEGNEUT ---------------------- Variables in the Equation ----------------------- Variable B S.E. Wald df Sig R Exp(B) LNNPOS .7712 .1179 42.7907 1 .0000 .1363 2.1624 LNPOS -.5137 .0475 116.8293 1 .0000 -.2288 .5983 Constant -3.9399 .1291 931.3828 1 .0000

  16. Predictive Value

  17. Some Recently Completed Work • Experiment: does reputation affect profit? • Many positives: Yes, but only a little (8.1%) • One or two negatives: No • Incentives for quality feedback provision • Can pay based on agreement among raters

  18. Studies Currently Underway • Feedback provision (empirical) • Reciprocation, altruism, and free riding • Dynamics: learning and selection (empirical) • Geography: trust and trustworthiness by state

  19. Design Space • Rating scales • Aggregation of ratings • Who rates? • Incentives for raters • Identification/Anonymity • Exchange partners • Evaluation providers

  20. Case Study • Goal: help Michigan non-profits select consultants and other service providers • Is this a good candidate for a reputation system?

  21. Case Study • Goal: help Michigan non-profits select consultants and other service providers • Is this a good candidate for a reputation system? • Interacting with strangers • Sellers (Exchange Partners) Vary • Skill • Effort • Ethics

  22. Case Study Design Choices • Rating scales • Aggregation of ratings • Who rates? • Incentives for raters • Identification/Anonymity • Exchange partners • Evaluation providers

  23. Summary • RS inform, incent, select • Opportunity for RS: interactions with strangers • Design space • Scales, aggregation, raters, incentives, anonymity

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