Building Web Reputation Systems Randy Farmer, Bryce Glass

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• Building Web Reputation Systems
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  • Preface
    • What Is This Book About?
    • Why Write a Book About Reputation?
    • Who Should Read This Book
    • Organization of This Book
      • Part I: Reputation Defined and Illustrated
      • Part II: Extended Elements and Applied Examples
      • Part III: Building Web Reputation Systems
      • Role-Based Reading (for Those in a Hurry)
    • Conventions Used in This Book
    • Safari Books Online
    • How to Contact Us
    • Acknowledgments
      • From Randy
      • From Bryce
  • I. Reputation Defined and Illustrated
    • 1. Reputation Systems Are Everywhere
      • An Opinionated Conversation
      • People Have Reputations, but So Do Things
      • Reputation Takes Place Within a Context
      • We Use Reputation to Make Better Decisions
      • The Reputation Statement
        • Explicit: Talk the Talk
        • Implicit: Walk the Walk
        • The Minimum Reputation Statement
      • Reputation Systems Bring Structure to Chaos
      • Reputation Systems Deeply Affect Our Lives
        • Local Reputation: It Takes a Village
        • Global Reputation: Collective Intelligence
        • FICO: A Study in Global Reputation and Its Challenges
        • Web FICO?
      • Reputation on the Web
        • Attention Doesnt Scale
        • Theres a Whole Lotta Crap Out There
        • People Are Good. Basically.
          • Know thy user
          • Honor creators, synthesizers, and consumers
          • Throw the bums out
        • The Reputation Virtuous Circle
        • Whos Using Reputation Systems?
        • Challenges in Building Reputation Systems
        • Related Subjects
        • Conceptualizing Reputation Systems
    • 2. A (Graphical) Grammar for Reputation
      • The Reputation Statement and Its Components
        • Reputation Sources: Who or What Is Making a Claim?
        • Reputation Claims: What Is the Targets Value to the Source? On What Scale?
        • Reputation Targets: What (or Who) Is the Focus of a Claim?
      • Molecules: Constructing Reputation Models Using Messages and Processes
        • Messages and Processes
        • Reputation Model Explained: Vote to Promote
        • Building on the Simplest Model
      • Complex Behavior: Containers and Reputation Statements As Targets
      • Solutions: Mixing Models to Make Systems
        • From Reputation Grammar to
  • II. Extended Elements and Applied Examples
    • 3. Building Blocks and Reputation Tips
      • Extending the Grammar: Building Blocks
        • The Data: Claim Types
          • Qualitative claim types
            • Text comments
            • Media uploads
            • Relevant external objects
          • Quantitative claim types
            • Normalized value
            • Rank value
            • Scalar value
        • Processes: Computing Reputation
          • Roll-ups: Counters, accumulators, averages, mixers, and ratios
            • Simple Counter
            • Reversible Counter
            • Simple Accumulator
            • Reversible Accumulator
            • Simple Average
            • Reversible Average
            • Mixer
            • Simple Ratio
            • Reversible Ratio
          • Transformers: Data normalization
            • Simple normalization (and weighted transform)
            • Scalar denormalization
            • External data transform
        • Routers: Messages, Decisions, and Termination
          • Common decision process patterns
            • Simple Terminator
            • Simple Evaluator
            • Terminating Evaluator
            • Message Splitter
            • Conjoint Message Delivery
          • Input
            • Typical inputs
            • Reputation statements as input
            • Periodic inputs
          • Output
            • Return values
            • Signals: Breaking out of the reputation framework
            • Logging
      • Practitioners Tips: Reputation Is Tricky
        • The Power and Costs of Normalization
        • Liquidity: You Wont Get Enough Input
        • Bias, Freshness, and Decay
          • Ratings bias effects
          • First-mover effects
          • Freshness and decay
        • Implementers Notes
      • Making Buildings from Blocks
    • 4. Common Reputation Models
      • Simple Models
        • Favorites and Flags
          • Vote to promote
          • Favorites
          • Report abuse
        • This-or-That Voting
        • Ratings
        • Reviews
        • Points
        • Karma
          • Participation karma
          • Quality karma
          • Robust karma
      • Combining the Simple Models
        • User Reviews with Karma
        • eBay Seller Feedback Karma
        • Flickr Interestingness Scores for Content Quality
      • When and Why Simple Models Fail
        • Party Crashers
        • Keep Your Barn Door Closed (but Expect Peeking)
          • Decay and delay
          • Provide a moving target
      • Reputation from Theory to Practice
  • III. Building Web Reputation Systems
    • 5. Planning Your Systems Design
      • Asking the Right Questions
        • What Are Your Goals?
          • User engagement
          • Establishing loyalty
          • Coaxing out shy advertisers
          • Improving content quality
        • Content Control Patterns
          • Web 1.0: Staff creates, evaluates, and removes
          • Bug report: Staff creates and evaluates, users remove
          • Reviews: Staff creates and removes, users evaluate
          • Surveys: Staff creates, users evaluate and remove
          • Submit-publish: Users create, staff evaluates and removes
          • Agents: Users create and remove, staff evaluates
          • Basic social media: Users create and evaluate, staff removes
          • The Full Monty: Users create, evaluate, and remove
        • Incentives for User Participation, Quality, and Moderation
          • Predictably irrational
          • Incentives and reputation
          • Altruistic or sharing incentives
            • Tit-for-tat and pay-it-forward incentives
            • Friendship incentives
            • Crusader, opinionated incentives, and know-it-all
          • Commercial incentives
            • Direct revenue incentives
            • Incentives through branding: Professional promotion
          • Egocentric incentives
            • Fulfillment incentives
            • Recognition incentives
            • Personal or private incentives: The quest for mastery
        • Consider Your Community
          • What are people there to do?
          • Is this a new community? Or an established one?
          • The competitive spectrum
      • Better Questions
    • 6. Objects, Inputs, Scope, and Mechanism
      • The Objects in Your System
        • Architect, Understand Thyself
          • Sowhat does your application do?
          • Perform an application audit
        • What Makes for a Good Reputable Entity?
          • People are interested in it
          • The decision investment is high
          • The entity has some intrinsic value worth enhancing
          • The entity should persist for some length of time
      • Determining Inputs
        • User Actions Make Good Inputs
          • Explicit claims
          • Implicit claims
        • But Other Types of Inputs Are Important, Too
        • Good Inputs
          • Emphasize quality, not simple activity
          • Rate the thing, not the person
          • Reward firsts, but not repetition
          • Use the right scale for the job
          • Match user expectations
        • Common Explicit Inputs
          • The ratings life cycle
            • The interface design of reputation inputs
          • Stars, bars, and letter grades
            • The schizophrenic nature of stars
            • Do I like you, or do I like like you
          • Two-state votes (thumb ratings)
          • Vote to promote: Digging, liking, and endorsing
          • User reviews
        • Common Implicit Inputs
          • Favorites, forwarding, and adding to a collection
            • Favorites
            • Forwarding
            • Adding to a collection
          • Greater disclosure
          • Reactions: Comments, photos, and media
      • Constraining Scope
        • Context Is King
        • Limit Scope: The Rule of Email
        • Applying Scope to Yahoo! EuroSport Message Board Reputation
      • Generating Reputation: Selecting the Right Mechanisms
        • The Heart of the Machine: Reputation Does Not Stand Alone
        • Common Reputation Generation Mechanisms and Patterns
          • Generating personalization reputation
          • Generating aggregated community ratings
            • Ranking large target sets (preference orders)
          • Generating participation points
            • Points as currency
          • Generating compound community claims
          • Generating inferred karma
      • Practitioners Tips: Negative Public Karma
      • Draw Your Diagram
    • 7. Displaying Reputation
      • How to Use a Reputation: Three Questions
      • Who Will See a Reputation?
        • To Show or Not to Show?
        • Personal Reputations: For the Owners Eyes Only
        • Personal and Public Reputations Combined
        • Public Reputations: Widely Visible
        • Corporate Reputations Are Internal Use Only: Keep Them Hush-hush
      • How Will You Use Reputation to Modify Your Sites Output?
        • Reputation Filtering
        • Reputation Ranking and Sorting
        • Reputation Decisions
      • Content Reputation Is Very Different from Karma
        • Content Reputation
        • Karma
          • Karma is complex, built of indirect inputs
          • Karma calculations are often opaque
          • Display karma sparingly
          • Karma caveats
      • Reputation Display Formats
      • Reputation Display Patterns
        • Normalized Score to Percentage
        • Points and Accumulators
        • Statistical Evidence
        • Levels
          • Numbered levels
          • Named levels
        • Ranked Lists
          • Leaderboard ranking
          • Top-X ranking
      • Practitioners Tips
        • Leaderboards Considered Harmful
          • What do you measure?
          • Whatever you do measure will be taken way too seriously
          • If it looks like a leaderboard and quacks like a leaderboard
          • Leaderboards are powerful and capricious
          • Who benefits?
      • Going Beyond Displaying Reputation
    • 8. Using Reputation: The Good, The Bad, and the Ugly
      • Up with the Good
        • Rank-Order Items in Lists and Search Results
        • Content Showcases
          • The human touch
      • Down with the Bad
        • Configurable Quality Thresholds
        • Expressing Dissatisfaction
      • Out with the Ugly
        • Reporting Abuse
          • Who watches the watchers?
      • Teach Your Users How to Fish
        • Inferred Reputation for Content Submissions
          • Just-in-time reputation calculation
        • A Private Conversation
        • Course-Correcting Feedback
      • Reputation Is Identity
        • On the User Profile
          • My Affiliations
          • My History
          • My Achievements
        • At the Point of Attribution
        • To Differentiate Within Listings
      • Putting It All Together
    • 9. Application Integration, Testing, and Tuning
      • Integrating with Your Application
        • Implementing Your Reputation Model
        • Rigging Inputs
        • Applied Outputs
        • Beware Feedback Loops!
        • Plan for Change
      • Testing Your System
        • Bench Testing Reputation Models
        • Environmental (Alpha) Testing Reputation Models
        • Predeployment (Beta) Testing Reputation Models
          • Performance: Testing scale
          • Confidence: Testing computation accuracy
          • Application optimization: Measuring use patterns
          • Feedback: Evaluating customers satisfaction
          • Value: Measuring ROI
      • Tuning Your System
        • Tuning for ROI: Metrics
          • Model tuning
          • Application tuning
        • Tuning for Behavior
          • Emergent effects and emergent defects
            • Defending against emergent defects
          • Keep great reputations scarce
        • Tuning for the Future
      • Learning by Example
    • 10. Case Study: Yahoo! Answers Community Content Moderation
      • What Is Yahoo! Answers?
        • A Marketplace for Questions and Yahoo! Answers
        • Attack of the Trolls
          • Time was a factor
          • Location, location, location
        • Built with Reputation
        • Avengers Assemble!
      • Initial Project Planning
        • Setting Goals
          • Cutting costs
          • Cleaning up the neighborhood
        • Who Controls the Content?
        • Incentives
        • The High-Level Project Model
      • Objects, Inputs, Scope, and Mechanism
        • The Objects
        • Limiting Scope
        • An Evolving Model
          • Iteration 1: Abuse reporting
            • Inputs
            • Mechanism and diagram
            • Analysis
          • Iteration 2: Karma for abuse reporters
            • Inputs
            • Mechanism and diagram
            • Analysis
          • Iteration 3: Karma for authors
            • Inputs
            • Mechanism and diagram
            • Analysis
          • Final design: Adding inferred karma
            • Inputs
            • Mechanism and diagram
            • Analysis
      • Displaying Reputation
        • Who Will See the Reputation?
        • How Will the Reputation Be Used to Modify Your Sites Output?
        • Is This Reputation for a Content Item or a Person?
      • Using Reputation: TheUgly
      • Application Integration, Testing, and Tuning
        • Application Integration
        • Testing Is Harder Than You Think
        • Lessons in Tuning: Users Protecting Their Power
      • Deployment and Results
      • Operational and Community Adjustments
      • Adieu
    • A. The Reputation Framework
      • Reputation Framework Requirements
        • Calculations: Static Versus Dynamic
          • Static: Performance, performance, performance
          • Dynamic: Reputation within social networks
        • Scale: Large Versus Small
        • Reliability: Transactional Versus Best-Effort
        • Model Complexity: Complex Versus Simple
        • Data Portability: Shared Versus Integrated
        • Optimistic Messaging Versus Request-Reply
          • Performance at scale
      • Framework Designs
        • The Invisible Reputation Framework: Fast, Cheap, and Out of Control
          • Requirements
          • Implementation details
          • Lessons learned
        • The Yahoo! Reputation Platform: Shared, Reliable Reputation at Scale
          • Yahoo! requirements
          • Yahoo! implementation details
            • High-level architecture
            • Messaging dispatcher
            • Model execution engine
            • External signaling interface
            • Reputation repository
            • Reputation query interface
          • Yahoo! lessons learned
      • Your Mileage May Vary
        • Recommendations for All Reputation Frameworks
    • B. Related Resources
      • Further Reading
      • Recommender Systems
      • Social Incentives
      • Patents
  • Index
  • About the Authors
  • Colophon
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