Index

A note on the digital index

A link in an index entry is displayed as the section title in which that entry appears. Because some sections have multiple index markers, it is not unusual for an entry to have several links to the same section. Clicking on any link will take you directly to the place in the text in which the marker appears.

A

abuse
of karma models, User Reviews with Karma
unacceptable user submissions, There’s a Whole Lotta Crap Out There
abuse reporting, Reporting Abuse
on Flickr, Flickr Interestingness Scores for Content Quality
report abuse model, Report abuse
simple system, Solutions: Mixing Models to Make Systems
watching the watchers, Who watches the watchers?
Yahoo! Answers, Application tuning, The High-Level Project Model
accumulators
display of, Points and Accumulators
reversible, Reversible Accumulator
simple, Simple Accumulator
achievements of users, My Achievements
adding to collections, Adding to a collection
advertisers, attracting, Coaxing out shy advertisers
affiliations and reputation, My Affiliations
agents content control pattern, Agents: Users create and remove, staff evaluates
aggregate source, Reputation Sources: Who or What Is Making a Claim?
aggregated community ratings, Generating aggregated community ratings
alpha testing reputation models, Environmental (Alpha) Testing Reputation Models
altruistic motivation, Incentives and reputation
altruistic or sharing incentives, Altruistic or sharing incentives–Crusader, opinionated incentives, and know-it-all
friendship, Friendship incentives
know-it-all, crusader, and opinionated, Crusader, opinionated incentives, and know-it-all
tit-for-tat and pay-it-forward, Tit-for-tat and pay-it-forward incentives
Amazon
karma example, top reviewer rankings, Top-X ranking
top reviewers, At the Point of Attribution
user reviews, User reviews
application integration, Application Integration, Testing, and Tuning–Plan for Change
avoiding feedback loops, Beware Feedback Loops!
implementing reputation model, Implementing Your Reputation Model
inputs, Rigging Inputs
planning for change, Plan for Change
Yahoo! Answers system, Application Integration, Testing, and Tuning
application optimization, Application optimization: Measuring use patterns
application tuning, Application tuning, Application tuning
(see also tuning reputation systems)
Ariely, Dan, Predictably irrational, Direct revenue incentives, Up with the Good
asynchronous activations, But Other Types of Inputs Are Important, Too
attention and massive scale of web content, Attention Doesn’t Scale
audits of reputation system applications, Perform an application audit
averages
problems with simple averages, Liquidity: You Won’t Get Enough Input
reversible, Reversible Average
simple, Simple Average

B

basic social media (content control pattern), Basic social media: Users create and evaluate, staff removes
best-effort reliability, Reliability: Transactional Versus Best-Effort
invisible reputation framework, Requirements
beta testing reputation models, Predeployment (Beta) Testing Reputation Models
bias, Liquidity: You Won’t Get Enough Input–First-mover effects
first-mover effects, First-mover effects
ratings bias effects, Bias, Freshness, and Decay
branding, Incentives through branding: Professional promotion
broken windows theory
online behavior and, Down with the Bad
Yahoo! Answers and, Deployment and Results
browser cookies, input from, But Other Types of Inputs Are Important, Too
bug reports (content control pattern), Bug report: Staff creates and evaluates, users remove
building blocks, Building Blocks and Reputation Tips–Logging
claim types, The Data: Claim Types
computing reputation, Processes: Computing Reputation–External data transform
routers, Routers: Messages, Decisions, and Termination–Logging

C

changes in reputation systems, Provide a moving target
planning for, Plan for Change
cheap versus free, Direct revenue incentives
Child Online Protection Act (COPA), Basic social media: Users create and evaluate, staff removes
Children’s Online Privacy and Protection Act (COPPA), Basic social media: Users create and evaluate, staff removes
claims, Reputation Claims: What Is the Target’s Value to the Source? On What Scale?, The Data: Claim Types–Scalar value
explicit, from user actions, Explicit claims
generating compound community claims, Generating compound community claims
implicit, from user actions, Implicit claims
qualitative, Qualitative claim types
media uploads, Media uploads
text comments, Text comments
quantitative, Quantitative claim types
normalized value, Normalized value
rank value, Rank value
scalar value, Scalar value
reliability of, Reliability: Transactional Versus Best-Effort
target or focus of, Reputation Targets: What (or Who) Is the Focus of a Claim?
types of, The Data: Claim Types
collections, adding to, Adding to a collection
commercial incentives, Commercial incentives–Incentives through branding: Professional promotion
branding and professional promotion, Incentives through branding: Professional promotion
direct revenue, Direct revenue incentives
commercial motivation, Incentives and reputation
community, Consider Your Community–The competitive spectrum
competitive spectrum, The competitive spectrum
engagement, metrics for, User engagement
new or established, Is this a new community? Or an established one?
purpose of, What are people there to do?
community interest rank, Building on the Simplest Model
community ratings, aggregated, Generating aggregated community ratings
competitive spectrum, The competitive spectrum
computation accuracy, testing, Confidence: Testing computation accuracy
computing reputation, Processes: Computing Reputation–External data transform
accumulators, Simple Accumulator
averages, Simple Average
counters, Simple Counter
mixers, Mixer
ratios, Simple Ratio
roll-ups, Roll-ups: Counters, accumulators, averages, mixers, and ratios
static versus dynamic, Calculations: Static Versus Dynamic
transformers and data normalization, Transformers: Data normalization
conjoint message delivery, Conjoint Message Delivery
Consumer Reports, example of compound community ratings and reviews, Generating compound community claims
consumers, Honor creators, synthesizers, and consumers
containers, Complex Behavior: Containers and Reputation Statements As Targets
content control patterns, Content Control Patterns–Incentives for User Participation, Quality, and Moderation
agents, Agents: Users create and remove, staff evaluates
basic social media, Basic social media: Users create and evaluate, staff removes
bug reports, Bug report: Staff creates and evaluates, users remove
Full Monty, The Full Monty: Users create, evaluate, and remove
reviews, Reviews: Staff creates and removes, users evaluate
submit-publish, Submit-publish: Users create, staff evaluates and removes
surveys, Surveys: Staff creates, users evaluate and remove
Web 1.0, Web 1.0: Staff creates, evaluates, and removes
content quality, There’s a Whole Lotta Crap Out There, There’s a Whole Lotta Crap Out There
(see also quality)
configurable thresholds for, Configurable Quality Thresholds
improving, Improving content quality
content reputation, Content Reputation
normalized percentages with summary count (example), Normalized Score to Percentage
content showcases, Content Showcases
safeguards for, The human touch
content, users’ expression of opinions about, Expressing Dissatisfaction
contexts of reputation, Reputation Takes Place Within a Context, Local Reputation: It Takes a Village, Molecules: Constructing Reputation Models Using Messages and Processes
constraining scope, Constraining Scope
data portability and, Data Portability: Shared Versus Integrated
dynamic reputation models, Calculations: Static Versus Dynamic
FICO and the Web, Web FICO?
importance of, Context Is King
limiting for karma, Karma caveats
reputation generation and, The Heart of the Machine: Reputation Does Not Stand Alone
thumb voting, Two-state votes (thumb ratings)
using to guide ratings scale used, Use the right scale for the job
corporate reputations, Corporate Reputations Are Internal Use Only: Keep Them Hush-hush
counters, Simple Counter
reversible, Reversible Counter
Craigslist
abuse reporting, Report abuse
creators, honoring, Honor creators, synthesizers, and consumers
credit scores
creating feedback loop, Beware Feedback Loops!
FICO, FICO: A Study in Global Reputation and Its Challenges
cron jobs, But Other Types of Inputs Are Important, Too
crusader incentives, Crusader, opinionated incentives, and know-it-all
currency, reputation points as, Points as currency
customer care corrections and operator overrides, But Other Types of Inputs Are Important, Too

D

data normalization (see normalization)
decay (time-based) in reputation models, Decay and delay
decaying reputation scores, Freshness and decay
decisions
based on reputation, Reputation Decisions
high investment in, The decision investment is high
process patterns (routers), Common decision process patterns
Delicious
lists on, Emergent effects and emergent defects
denormalization, Transformers: Data normalization, The Power and Costs of Normalization
scalar, Scalar denormalization
Digg
accumulators display, Points and Accumulators
benefit to user, Explicit claims
design and voting behavior, Emergent effects and emergent defects
display of reputation scores, To Show or Not to Show?
vote-to-promote model, Molecules: Constructing Reputation Models Using Messages and Processes, Building on the Simplest Model, Vote to promote: Digging, liking, and endorsing
Digital Copyright Millennium Act (DCMA), Basic social media: Users create and evaluate, staff removes
direct revenue incentives, Direct revenue incentives
displaying reputation, Displaying Reputation–Going Beyond Displaying Reputation
corporate reputations, Corporate Reputations Are Internal Use Only: Keep Them Hush-hush
formats, Reputation Display Formats
harmful effects of leaderboards, Leaderboards Considered Harmful–Who benefits?
patterns, Reputation Display Patterns–Leaderboards Considered Harmful
levels, Levels–Ranked Lists
normalized score to percentage, Normalized Score to Percentage
points and accumulators, Points and Accumulators
statistical evidence, Statistical Evidence
personal and public reputations combined, Personal and Public Reputations Combined
personal reputations, Personal Reputations: For the Owner’s Eyes Only
questions on, How to Use a Reputation: Three Questions
ranked lists, Ranked Lists
to show or not to show, To Show or Not to Show?
Yahoo! Answers community content moderation, Displaying Reputation
dollhouse mafia, Practitioner’s Tips: Negative Public Karma
drop-shippers (on eBay), Party Crashers
dynamic calculations, Calculations: Static Versus Dynamic
invisible reputation framework, Requirements

E

eBay
drop-shippers on, Party Crashers
seller feedback model, Quality karma, eBay Seller Feedback Karma–eBay Seller Feedback Karma
sellers, negative public karma and, Practitioner’s Tips: Negative Public Karma
egocentric incentives, Egocentric incentives–Personal or private incentives: The quest for mastery
fulfillment, Fulfillment incentives
quest for mastery, Personal or private incentives: The quest for mastery
recognition, Recognition incentives
egocentric motivation, Incentives and reputation
email rule, for reputation input, Limit Scope: The Rule of Email
emergent defects, Emergent effects and emergent defects
defending against, Defending against emergent defects
emergent effects, Emergent effects and emergent defects
entities (see reputable entities)
explicit claims, Explicit claims
explicit inputs, Common Explicit Inputs–User reviews
interface design of inputs, The interface design of reputation inputs
problems with star ratings, The schizophrenic nature of stars
ratings life cycle, The ratings life cycle
ratings mechanisms, Stars, bars, and letter grades
user reviews, User reviews
vote-to-promote, Vote to promote: Digging, liking, and endorsing
explicit reputation statements, Explicit: Talk the Talk
external data transform, External data transform
external objects, claims based on, Relevant external objects
external signaling interface, External signaling interface
external trust databases, input from, But Other Types of Inputs Are Important, Too

F

Facebook, Friendship incentives
I like this link feature, Vote to promote: Digging, liking, and endorsing
favorites-and-flags model, Favorites and Flags
favorites, Favorites, Favorites, forwarding, and adding to a collection
vote-to-promote variant, Vote to promote
feedback
educating users to become good contributors, Course-Correcting Feedback
evaluating customer satisfaction, Feedback: Evaluating customer’s satisfaction
immediate, Explicit claims
feedback loops, Beware Feedback Loops!
FICO credit score, global reputation study, FICO: A Study in Global Reputation and Its Challenges
filtering (reputation), Reputation Filtering
fire-and-forget messaging, Performance at scale
first-mover effects, First-mover effects
firsts, rewarding, Reward firsts, but not repetition
flagging
abusive content, Report abuse
on Flickr, Flickr Interestingness Scores for Content Quality
on Yahoo! Answers, Inputs
flexibility in reputation systems, Provide a moving target
Flickr
feedback to contributors, Course-Correcting Feedback
interestingness algorithm, Text comments
interestingness score for content quality, Flickr Interestingness Scores for Content Quality–Flickr Interestingness Scores for Content Quality
reputation display and, To Show or Not to Show?
forwarding, Forwarding
frameworks (see reputation frameworks)
free versus cheap, Direct revenue incentives
freeform content, Reputation Display Formats
freshness and decay, Freshness and decay
friendship incentives, Friendship incentives
fulfillment incentives, Fulfillment incentives
Full Monty (content control pattern), The Full Monty: Users create, evaluate, and remove

G

game currencies, reputation points as, Points as currency
global reputation, Global Reputation: Collective Intelligence
FICO, FICO: A Study in Global Reputation and Its Challenges
goals, defining for reputation system, What Are Your Goals?–Improving content quality
Google
Analytics, use of personal reputations, Personal Reputations: For the Owner’s Eyes Only
Answers, direct revenue incentives, Direct revenue incentives
Orkut, To Show or Not to Show?, Leaderboards are powerful and capricious
greater disclosure (adding information), Greater disclosure

H

Hawthorne effect, Tuning for ROI: Metrics
histories, reputation and, Reputation Is Identity
user profiles, My History

I

iconic numbered levels, Numbered levels
identity spoofing, Reputation Is Identity
identity, reputation as, Reputation Is Identity–Putting It All Together
contributor ranks in listings, To Differentiate Within Listings
user profiles, On the User Profile
user reputation in context of contribution, At the Point of Attribution
implicit claims, Implicit claims
implicit inputs, Common Implicit Inputs–Constraining Scope
adding to collection, Adding to a collection
favorites, Favorites, forwarding, and adding to a collection
forwarding, Forwarding
greater disclosure (adding information), Greater disclosure
reactions to reputable entities, Reactions: Comments, photos, and media
implicit reputation statements, Implicit: Walk the Walk
imprinting, Up with the Good
incentives, Incentives for User Participation, Quality, and Moderation–Personal or private incentives: The quest for mastery
altruistic or sharing incentives, Altruistic or sharing incentives–Crusader, opinionated incentives, and know-it-all
categories of, Incentives and reputation
commercial, Commercial incentives–Incentives through branding: Professional promotion
branding or professional promotion, Incentives through branding: Professional promotion
direct revenue, Direct revenue incentives
determining type for your system, Asking the Right Questions
egocentric, Egocentric incentives–Personal or private incentives: The quest for mastery
fulfillment, Fulfillment incentives
mastery incentives, Personal or private incentives: The quest for mastery
recognition, Recognition incentives
social incentives, information resources, Social Incentives
social versus market exchanges, Predictably irrational
for user engagement, User engagement, Reviews: Staff creates and removes, users evaluate
inferred karma, Data Portability: Shared Versus Integrated
generating, Generating inferred karma
in Yahoo! Answers reputation model, Final design: Adding inferred karma
inferred reputation, Inferred Reputation for Content Submissions
just-in-time reputation calculation, Just-in-time reputation calculation
input, Input, Determining Inputs–Constraining Scope
automating simulated inputs, Bench Testing Reputation Models
best practices for good inputs, Good Inputs
common explicit inputs, Common Explicit Inputs–User reviews
common implicit inputs, Common Implicit Inputs–Constraining Scope
constraining scope, Constraining Scope–Applying Scope to Yahoo! EuroSport Message Board Reputation
implementing inputs, Rigging Inputs
items to be used as, Perform an application audit
reversible, Reliability: Transactional Versus Best-Effort
from sources other than user actions, But Other Types of Inputs Are Important, Too
user actions, User Actions Make Good Inputs
explicit claims, Explicit claims
implicit claims, Implicit claims
Yahoo! Answers community content moderation model, Inputs, Inputs, Inputs, Inputs
input events (reputation message), Messages and Processes
integration, application (see application integration)
interest, Building on the Simplest Model
measured by response to an entity, Reactions: Comments, photos, and media
interestingness scores (Flickr), Flickr Interestingness Scores for Content Quality–Flickr Interestingness Scores for Content Quality
interface design of inputs, The interface design of reputation inputs
invisible reputation framework, The Invisible Reputation Framework: Fast, Cheap, and Out of Control–Lessons learned
implementation details, Implementation details
lessons from, Lessons learned
requirements, Requirements
iTunes rating system, Explicit claims

J

J-curves, Ratings bias effects
just-in-time inputs, But Other Types of Inputs Are Important, Too
just-in-time reputation calculation, Just-in-time reputation calculation

K

karma, What Is This Book About?, Karma
abuse reporters on Yahoo! Answers, Iteration 2: Karma for abuse reporters
authors on Yahoo! Answers, Iteration 3: Karma for authors
caveats, Karma caveats
complexity of, Karma is complex, built of indirect inputs
display examples, Normalized Score to Percentage–Top-X ranking
displaying sparingly, Display karma sparingly
eBay seller feedback karma, eBay Seller Feedback Karma–eBay Seller Feedback Karma
generating inferred karma, Generating inferred karma–Practitioner’s Tips: Negative Public Karma
inferred karma in Yahoo! Answers, Final design: Adding inferred karma
negative public karma, Practitioner’s Tips: Negative Public Karma
rating the content, not the person, Rate the thing, not the person
Slashdot, Display karma sparingly
user as target, Reputation Targets: What (or Who) Is the Focus of a Claim?
karma models, Karma
abuse of, User Reviews with Karma
participation karma, Participation karma
participation points, Generating participation points
quality karma, Quality karma
ratings-and-reviews with karma, User Reviews with Karma–User Reviews with Karma
robust karma, Robust karma
know-it-all incentives, Crusader, opinionated incentives, and know-it-all

L

leaderboards, Leaderboard ranking
content showcases and, Content Showcases
discouraging new contributors, First-mover effects
harmful effects of, Leaderboards Considered Harmful–Who benefits?
top-X, Top-X ranking
use with egocentric incentives, Recognition incentives
legal issues and content removal by staff, Basic social media: Users create and evaluate, staff removes
Level of Activity, Building on the Simplest Model
levels in reputation display, Levels–Ranked Lists
named levels, Named levels
numbered levels, Numbered levels
LinkedIn
completeness of profiles, A Private Conversation
user profile with group affiliations, My Affiliations
liquidity compensation algorithm, Liquidity: You Won’t Get Enough Input
lists, Rank-Order Items in Lists and Search Results
(see also ranked lists)
emergent effect on Delicious, Emergent effects and emergent defects
rank-order items in, Rank-Order Items in Lists and Search Results
local reputation, Local Reputation: It Takes a Village
logging, Logging
loyalty, establishing, Establishing loyalty

M

market norms, incentives and, Predictably irrational
mastery incentives, Personal or private incentives: The quest for mastery
media uploads, Media uploads
messages, Processes: Computing Reputation
routing, Routers: Messages, Decisions, and Termination–Conjoint Message Delivery
messaging
invisible reputation framework, Requirements
optimistic versus request-reply, Optimistic Messaging Versus Request-Reply
Yahoo! Reputation Platform, Yahoo! requirements
messaging dispatcher, Yahoo! Reputation Platform, Messaging dispatcher
metadata, Reputation Display Formats
mixers, Mixer
models (see reputation models)
moderation, incentives for (see incentives)
motivation (see incentives)

N

named levels in reputation display, Named levels
negative public karma, Practitioner’s Tips: Negative Public Karma
Sims Online game, Practitioner’s Tips: Negative Public Karma
negative reputation systems, The Reputation Virtuous Circle
normalization, Transformers: Data normalization
power and costs of, The Power and Costs of Normalization
normalized scores, Reputation Claims: What Is the Target’s Value to the Source? On What Scale?, Reputation Display Formats
display as percentages, Normalized Score to Percentage
normalized values, Normalized value
numbered levels in reputation display, Numbered levels

O

objects in reputation systems, Objects, Inputs, Scope, and Mechanism–The entity should persist for some length of time
application architecture, The Objects in Your System–Perform an application audit
performing application audit, Perform an application audit
reputable entities, What Makes for a Good Reputable Entity?–The entity should persist for some length of time
what the application does, So…what does your application do?
Yahoo! Answers community content moderation, The Objects
operator overrides, But Other Types of Inputs Are Important, Too
opinionated incentives, Crusader, opinionated incentives, and know-it-all
optimistic messaging, Optimistic Messaging Versus Request-Reply
Yahoo! Reputation Platform, Yahoo! requirements
Orkut, Leaderboards are powerful and capricious
reputation display, To Show or Not to Show?
output, Output
automating simulated reputation output events, Bench Testing Reputation Models
implementing, Applied Outputs

P

participation incentives (see incentives)
participation karma model, Participation karma
participation points, Points and Accumulators
generating, Generating participation points
patents, Patents
pay-it-forward incentives, Tit-for-tat and pay-it-forward incentives
people showcases, Content Showcases
percentages
normalized scores displayed as, Normalized Score to Percentage
performance
stress testing of, Bench Testing Reputation Models
testing for scale, Performance: Testing scale
personal or private egocentric incentives, Personal or private incentives: The quest for mastery
personal reputations, Personal Reputations: For the Owner’s Eyes Only, A Private Conversation
personalization reputation, generating, Generating personalization reputation
points
as currency, Points as currency
display of, Points and Accumulators
generating participation points, Generating participation points
simple model, Points
in Yahoo! Answers, Built with Reputation
portability of data, Data Portability: Shared Versus Integrated
positive reputations, The Reputation Virtuous Circle
practitioner’s tips, Practitioner’s Tips: Reputation Is Tricky–Making Buildings from Blocks
bias, freshness, and decay, Bias, Freshness, and Decay–Freshness and decay
harmful effects of leaderboards, Leaderboards Considered Harmful–Who benefits?
implementation notes, Implementer’s Notes
liquidity and input, Liquidity: You Won’t Get Enough Input
negative public karma, Practitioner’s Tips: Negative Public Karma
normalization, The Power and Costs of Normalization
predeployment (beta) testing reputation models, Predeployment (Beta) Testing Reputation Models
Predictably Irrational, Predictably irrational, Direct revenue incentives, Up with the Good
preference ordering, Ranking large target sets (preference orders)
primary value for contributions, Explicit claims
problem users, excluding, Throw the bums out
professional promotion, Incentives through branding: Professional promotion
public reputations, Public Reputations: Widely Visible

Q

qualitative claims, Reputation Claims: What Is the Target’s Value to the Source? On What Scale?, Qualitative claim types
media uploads, Media uploads
relevant external objects, Relevant external objects
text comments, Text comments
quality
configurable thresholds, Configurable Quality Thresholds
of content, There’s a Whole Lotta Crap Out There
emphasizing over simple activity, Emphasize quality, not simple activity
enforcing minimum editorial quality, Basic social media: Users create and evaluate, staff removes
Flickr interestingness scores for, Flickr Interestingness Scores for Content Quality–Flickr Interestingness Scores for Content Quality
improving content quality, Improving content quality
incentives for (see incentives)
measurement of, leaderboards and, What do you measure?
simple karma model, Quality karma
quantitative claims, Reputation Claims: What Is the Target’s Value to the Source? On What Scale?
normalized value, Normalized value
rank value, Rank value
raw scores, Reputation Claims: What Is the Target’s Value to the Source? On What Scale?
scalar value, Scalar value
quest for mastery, Personal or private incentives: The quest for mastery

R

rank values, Rank value
ranked lists, Ranked Lists, Rank-Order Items in Lists and Search Results
leaderboards, Leaderboard ranking
harmful effects of, Leaderboards Considered Harmful–Who benefits?
top-X, Top-X ranking
rankings, Reputation Ranking and Sorting
leaderboard, Leaderboard ranking
preference ordering, Ranking large target sets (preference orders)
top-X, Top-X ranking
ratings
aggregated community ratings, Generating aggregated community ratings
differing interpretations of, Do I like you, or do I “like” like you
entering versus displaying, Stars, bars, and letter grades
freshness and decay, Freshness and decay
life cycle of, The ratings life cycle
rating the content, not the person, Rate the thing, not the person
simple model, Ratings
star ratings, The schizophrenic nature of stars
two-state votes (thumbs ratings), Two-state votes (thumb ratings)
using right scale, Use the right scale for the job
ratings bias effects, Bias, Freshness, and Decay
ratings-and-reviews reputation models, Molecules: Constructing Reputation Models Using Messages and Processes
compound community claims mechanisms and, Generating compound community claims
input events, Messages and Processes
reviews that others can rate, Complex Behavior: Containers and Reputation Statements As Targets
Was this helpful? feedback mechanism, User Reviews with Karma
ratings-and-reviews with karma model, User Reviews with Karma–User Reviews with Karma
ratios
reversible, Reversible Ratio
simple, Simple Ratio
raw scores, Reputation Claims: What Is the Target’s Value to the Source? On What Scale?, Reputation Display Formats
raw sum of votes, Reputation Model Explained: Vote to Promote, Building on the Simplest Model
reactions to an entity, Reactions: Comments, photos, and media
recognition incentives, Recognition incentives
recommender systems, Related Subjects
resources for information, Recommender Systems
reliability in reputation frameworks
invisible reputation framework, Requirements
transactional versus best-effort, Reliability: Transactional Versus Best-Effort
Yahoo! Reputation Platform, Yahoo! requirements
repetition, limiting, Reward firsts, but not repetition
report abuse model, Report abuse
Yahoo! Answers community content moderation, An Evolving Model, Operational and Community Adjustments
republishing actions (on Flickr), Flickr Interestingness Scores for Content Quality
reputable entities, We Use Reputation to Make Better Decisions, Reputation Sources: Who or What Is Making a Claim?
as targets of claims, Reputation Targets: What (or Who) Is the Focus of a Claim?
characteristics of, What Makes for a Good Reputable Entity?–The entity should persist for some length of time
high-investment decision, The decision investment is high
interest to users, People are interested in it
intrinsic value worth enhancing, The entity has some intrinsic value worth enhancing
persistence over time, The entity has some intrinsic value worth enhancing
reactions to, Reactions: Comments, photos, and media
reputation
as identity, Reputation Is Identity–Putting It All Together
context for, Reputation Takes Place Within a Context
defined, What Is This Book About?
displaying (see displaying reputation)
incentives and, Incentives and reputation
of people and things, An Opinionated Conversation
resources for information, Further Reading
use in decision making, We Use Reputation to Make Better Decisions
on the Web, Reputation on the Web
reputation context (see contexts of reputation)
reputation frameworks, Solutions: Mixing Models to Make Systems, The Reputation Framework–Recommendations for All Reputation Frameworks
designs, Framework Designs–Yahoo! lessons learned
invisible framework, The Invisible Reputation Framework: Fast, Cheap, and Out of Control–Lessons learned
Yahoo! Reputation Platform, The Yahoo! Reputation Platform: Shared, Reliable Reputation at Scale–Yahoo! lessons learned
recommendations for all, Recommendations for All Reputation Frameworks
requirements, Reputation Framework Requirements–Performance at scale
calculations, static or dynamic, Calculations: Static Versus Dynamic
model complexity, Model Complexity: Complex Versus Simple
optimistic or request-reply messaging, Optimistic Messaging Versus Request-Reply
portability of data, Data Portability: Shared Versus Integrated
reliability, Reliability: Transactional Versus Best-Effort
scale, Scale: Large Versus Small
reputation generation mechanisms and patterns, Generating Reputation: Selecting the Right Mechanisms–Practitioner’s Tips: Negative Public Karma
aggregated community ratings, Generating aggregated community ratings
compound community claims, Generating compound community claims
context of reputation, The Heart of the Machine: Reputation Does Not Stand Alone
inferred karma, Generating inferred karma–Practitioner’s Tips: Negative Public Karma
participation points, Generating participation points
personalization reputation, Generating personalization reputation
points as currency, Points as currency
preference ordering, Ranking large target sets (preference orders)
reputation messages, Messages and Processes
reputation models, Molecules: Constructing Reputation Models Using Messages and Processes–Building on the Simplest Model
bench testing, Bench Testing Reputation Models
building on simplest model, Building on the Simplest Model
combining simple models, Combining the Simple Models–Flickr Interestingness Scores for Content Quality
eBay seller feedback karma, eBay Seller Feedback Karma–eBay Seller Feedback Karma
user reviews with karma, User Reviews with Karma–User Reviews with Karma
complex versus simple, Model Complexity: Complex Versus Simple
dynamic and static, Calculations: Static Versus Dynamic
environmental (alpha) testing, Environmental (Alpha) Testing Reputation Models
execution engine, Yahoo! platform, Model execution engine
failures of simple models, When and Why Simple Models Fail–Provide a moving target
disclosure of details about system, Keep Your Barn Door Closed (but Expect Peeking)
masking workings of algorithms, Keep Your Barn Door Closed (but Expect Peeking)
party crashers, Party Crashers
favorites and flags, Favorites and Flags
implementing, Implementing Your Reputation Model
karma, Karma
messages and processes, Messages and Processes
mixing to make systems, Complex Behavior: Containers and Reputation Statements As Targets
points, Points
predeployment (beta) testing, Predeployment (Beta) Testing Reputation Models
ratings, Ratings
reviews, Reviews
this-or-that voting, This-or-That Voting
tuning, Model tuning
vote-to-promote, Reputation Model Explained: Vote to Promote
Yahoo! Answers, community content moderation, The High-Level Project Model
reputation processes, Messages and Processes
abuse reporting system, Solutions: Mixing Models to Make Systems
calculate helpful score, Complex Behavior: Containers and Reputation Statements As Targets
computing reputation, Processes: Computing Reputation–External data transform
Yahoo! Answers community content moderation, Mechanism and diagram
reputation query interface, Reputation query interface
reputation repository (Yahoo! platform), Reputation repository
reputation statements, We Use Reputation to Make Better Decisions, A (Graphical) Grammar for Reputation
claims, Reputation Claims: What Is the Target’s Value to the Source? On What Scale?
explicit, Explicit: Talk the Talk
implicit, Implicit: Walk the Walk
as input, Reputation statements as input
shared versus integrated, Data Portability: Shared Versus Integrated
source, target, and claim, The Minimum Reputation Statement
sources, Reputation Sources: Who or What Is Making a Claim?
aggregate, Reputation Sources: Who or What Is Making a Claim?
user as, Reputation Sources: Who or What Is Making a Claim?
targets, Reputation Targets: What (or Who) Is the Focus of a Claim?
as targets of other reputation statements, Reputation Targets: What (or Who) Is the Focus of a Claim?
reputation systems
attention and massive scale of web content, Attention Doesn’t Scale
challenges in building, Challenges in Building Reputation Systems
conceptualizing, Conceptualizing Reputation Systems
context and, Web FICO?
defined, Solutions: Mixing Models to Make Systems
designing, Planning Your System’s Design–Better Questions
asking right questions and defining goals, Asking the Right Questions–Improving content quality
considering your community, Consider Your Community–The competitive spectrum
content control patterns, Content Control Patterns–Incentives for User Participation, Quality, and Moderation
incentives for user participation, quality, and moderation, Incentives for User Participation, Quality, and Moderation–Personal or private incentives: The quest for mastery
global reputation, Global Reputation: Collective Intelligence
FICO, FICO: A Study in Global Reputation and Its Challenges
local reputation, Local Reputation: It Takes a Village
mixing models to make, Complex Behavior: Containers and Reputation Statements As Targets
objects in (see objects in reputation systems)
project planning for Yahoo! Answers, Initial Project Planning
prominent consumer websites using, What Is This Book About?
related subjects, Related Subjects
reputation statement and its components, The Reputation Statement and Its Components
understanding your users, People Are Good. Basically.
use on top websites, Who’s Using Reputation Systems?
virtuous circle from quality contributions, Throw the bums out
Yahoo! Answers (see Yahoo! Answers)
request-reply messaging, Optimistic Messaging Versus Request-Reply
invisible reputation framework, Requirements
resources for further information, Related Resources
return values, Return values
revenue exposure, Basic social media: Users create and evaluate, staff removes
reversible accumulator, Reversible Accumulator
reversible average, Reversible Average
reversible counter, Reversible Counter
reversible ratio, Reversible Ratio
reviews, Reputation Targets: What (or Who) Is the Focus of a Claim?
(see also ratings-and-reviews reputation models)
Amazon as example (see Amazon)
content control pattern, Reviews: Staff creates and removes, users evaluate
simple model, Reviews
staff creating and removing, users evaluating, Reviews: Staff creates and removes, users evaluate
user reviews as explicit input, User reviews
user reviews with karma, User Reviews with Karma–User Reviews with Karma
robust karma model, Robust karma
ROI
measuring in predeployment testing, Value: Measuring ROI
tuning for, metrics, Tuning for ROI: Metrics–Application tuning
roll-ups, Messages and Processes, Processes: Computing Reputation, Roll-ups: Counters, accumulators, averages, mixers, and ratios–Reversible Ratio
accumulators, Simple Accumulator
averages, Simple Average
counters, Simple Counter
mixers, Mixer
ratios, Simple Ratio
routers, Routers: Messages, Decisions, and Termination–Logging
decision process patterns, Common decision process patterns
input, Input
output, Output

S

scalar values, Scalar value
combining normalized, The Power and Costs of Normalization
denormalization, Scalar denormalization
scale, Scale: Large Versus Small
invisible reputation framework, Requirements
using right scale, Use the right scale for the job
Yahoo! Reputation Platform, Yahoo! requirements
scope, constraining, Constraining Scope–Applying Scope to Yahoo! EuroSport Message Board Reputation
importance of context, Context Is King
rule of email in reputation input, Limit Scope: The Rule of Email
Yahoo! Answers community content moderation, Limiting Scope
Yahoo! EuroSport message board reputation, Applying Scope to Yahoo! EuroSport Message Board Reputation
search engine optimization (SEO), Yahoo! requirements
search relevance, Related Subjects
search results, rank-order items in, Rank-Order Items in Lists and Search Results
seller feedback karma (eBay), eBay Seller Feedback Karma–eBay Seller Feedback Karma
session data, input from, But Other Types of Inputs Are Important, Too
ShareTV.org, use of participation points, Generating participation points
showcases for content, Content Showcases
safeguards for, The human touch
signals, Signals: Breaking out of the reputation framework
external signaling interface, External signaling interface
simple accumulator, Reputation Model Explained: Vote to Promote, Simple Accumulator
simple averages, Simple Average
problems with, Liquidity: You Won’t Get Enough Input
simple counter, Simple Counter
simple ratio, Simple Ratio
Sims Online, Practitioner’s Tips: Negative Public Karma
Slashdot
karma display, Display karma sparingly
quality thresholds, Configurable Quality Thresholds
social and market norms, incentives and, Predictably irrational
social games, Points as currency
social incentives, resources for information, Social Incentives
social media
attempt to integrate into Yahoo! Sports, Context Is King
basic social media content control pattern, Basic social media: Users create and evaluate, staff removes
harmful effects of leaderboards, Leaderboards Considered Harmful–Who benefits?
news sites, vote-to-promote model, Vote to promote: Digging, liking, and endorsing
Orkut, Leaderboards are powerful and capricious
reputation within social networks, Dynamic: Reputation within social networks
social network filters, Related Subjects
social networking relationships, input from, But Other Types of Inputs Are Important, Too
sources, Reputation Sources: Who or What Is Making a Claim?
spammers
excluding, Throw the bums out
trolls versus, Attack of the Trolls
star ratings
differing interpretations of, Do I like you, or do I “like” like you
problems with, Stars, bars, and letter grades
stars-and-bars display pattern, Numbered levels
static reputation calculations, Static: Performance, performance, performance
Yahoo! Reputation Platform, Yahoo! requirements
statistical evidence in reputation display, Statistical Evidence
stored reputation value, Messages and Processes
submit-publish content control pattern, Submit-publish: Users create, staff evaluates and removes
summary count, Reputation Display Formats
surveys content control pattern, Surveys: Staff creates, users evaluate and remove
synthesizers, Honor creators, synthesizers, and consumers

T

tagging (on Flickr), Flickr Interestingness Scores for Content Quality, Flickr Interestingness Scores for Content Quality
targets, Reputation Targets: What (or Who) Is the Focus of a Claim?
containers and reputation statements, Complex Behavior: Containers and Reputation Statements As Targets
termination (routers), Simple Terminator
testing reputation systems, Testing Your System–Value: Measuring ROI
bench testing reputation models, Bench Testing Reputation Models
environmental (alpha) testing reputation models, Environmental (Alpha) Testing Reputation Models
predeployment (beta) testing reputation models, Predeployment (Beta) Testing Reputation Models
Yahoo! Answers model, Testing Is Harder Than You Think
text comments, Text comments
this-or-that voting, This-or-That Voting
thumbs ratings, Two-state votes (thumb ratings), Expressing Dissatisfaction
time-activated inputs, But Other Types of Inputs Are Important, Too
tit-for-tat incentives, Tit-for-tat and pay-it-forward incentives
top-X ranking, Top-X ranking
transaction-level reliability in reputation frameworks, Reliability: Transactional Versus Best-Effort
Yahoo! Reputation Platform, Yahoo! requirements
transformation, normalized values, The Power and Costs of Normalization
transformers, Transformers: Data normalization
transitional values for normalized data, Reputation Display Formats
trolls
attack on Yahoo! Answers, Attack of the Trolls
excluding, Throw the bums out
spammers versus, Attack of the Trolls
tuning reputation systems, Tuning Your System–Tuning for the Future
excessive tuning and Hawthorne effect, Tuning for ROI: Metrics
for behavior, Tuning for Behavior–Tuning for the Future
defending against emergent defects, Defending against emergent defects
emergent effects and defects, Emergent effects and emergent defects
keeping great reputations scarce, Keep great reputations scarce
for ROI, Tuning for ROI: Metrics–Application tuning
for the future, Tuning for the Future
Yahoo! Answers, Lessons in Tuning: Users Protecting Their Power
Twitter, Friendship incentives
display of community member stats, If it looks like a leaderboard and quacks like a leaderboard…
two-state votes (thumbs ratings), Two-state votes (thumb ratings)

U

use patterns, measuring, Application optimization: Measuring use patterns
user engagement, goals for, User engagement
user profiles, On the User Profile
achievements, My Achievements
affiliations, My Affiliations
historical information, My History
user reputation (see karma)
user-generated content, People Are Good. Basically.
users
as source, Reputation Sources: Who or What Is Making a Claim?
full control over content, The Full Monty: Users create, evaluate, and remove
matching expectations with appropriate rating scale, Match user expectations
as targets of reputation claims, Reputation Targets: What (or Who) Is the Focus of a Claim?
understanding and managing, Know thy user
using reputation, Using Reputation: The Good, The Bad, and the Ugly–Putting It All Together
abuse reporting, Out with the Ugly
educating users to become better contributors, Teach Your Users How to Fish
course-correcting feedback, Course-Correcting Feedback
inferred reputation for submissions, Inferred Reputation for Content Submissions
personal reputations, A Private Conversation
minimizing or downplaying poor content, Down with the Bad–Expressing Dissatisfaction
promoting and surfacing good content, Up with the Good–The human touch
reputation as identity, Reputation Is Identity–Putting It All Together

V

viewer activities (Flickr), Flickr Interestingness Scores for Content Quality
Vimeo, Content Showcases
virtuous circle created by quality contributions, The Reputation Virtuous Circle
vote-to-promote reputation model, Reputation Model Explained: Vote to Promote, Vote to promote, Vote to promote: Digging, liking, and endorsing
Digg.com, fuller representation of, Building on the Simplest Model

W

Was this helpful? feedback mechanism, User Reviews with Karma
Web 1.0 content control pattern, Web 1.0: Staff creates, evaluates, and removes
websites using reputation systems, Who’s Using Reputation Systems?
weighted transform, Simple normalization (and weighted transform)
weighted voting model, Solutions: Mixing Models to Make Systems
weighting, Building on the Simplest Model
wiki for this book, A (Graphical) Grammar for Reputation
WikiAnswers.com, Generating inferred karma
karma display example, Named levels
World of Warcraft
egocentric incentives, Egocentric incentives
identities, Reputation Is Identity

Y

Yahoo!
360° social network, Friendship incentives
Autos Custom ratings, Ratings bias effects
EuroSport message board reputation, Applying Scope to Yahoo! EuroSport Message Board Reputation
Local, reviews of establishments, Text comments
reputation platform, The Yahoo! Reputation Platform: Shared, Reliable Reputation at Scale–Yahoo! lessons learned
external signaling interface, External signaling interface
high-level architecture, High-level architecture
implementation details, Yahoo! implementation details
lessons from, Yahoo! lessons learned
model execution engine, Model execution engine
reputation query interface, Reputation query interface
reputation repository, Reputation repository
requirements, Yahoo! requirements
Reputation Platform
messaging dispatcher, Messaging dispatcher
Sports, attempt to integrate social media, Context Is King
UK Sports Community Stars module, Content Showcases
Yahoo! Answers, Case Study: Yahoo! Answers Community Content Moderation–Adieu
application integration, testing, and tuning, Application Integration, Testing, and Tuning–Lessons in Tuning: Users Protecting Their Power
attack by trolls, Attack of the Trolls
content control, Who Controls the Content?
deployment and results for new system, Deployment and Results
description of, What Is Yahoo! Answers?
displaying source of statistical evidence, Statistical Evidence
inferred karma, Generating inferred karma
leaderboard rankings, Leaderboard ranking
marketplace for questions and answers, A Marketplace for Questions and Yahoo! Answers
objects, inputs, scope, and mechanism in reputation system, Objects, Inputs, Scope, and Mechanism–Analysis
operational and community adjustments for new system, Operational and Community Adjustments
participation points, Points and Accumulators
project planning for community content moderation, Initial Project Planning–The High-Level Project Model
reputation system, Built with Reputation
Star mechanism and abuse reporting, Application tuning
teams handling abuse problem, Avengers Assemble!
Yelp
community and public reputations, Public Reputations: Widely Visible
egocentric incentives for user engagement, Reviews: Staff creates and removes, users evaluate
YouTube
leaderboard ranking for most viewed videos, Leaderboard ranking
massive amounts of content on, Attention Doesn’t Scale
statistical data on video popularity, Statistical Evidence
Symphony Orchestra contest, Submit-publish: Users create, staff evaluates and removes
video responses, Media uploads, Reactions: Comments, photos, and media

Z

zero price effect, Direct revenue incentives
Zynga, Mafia Wars social game, Points as currency
..................Content has been hidden....................

You can't read the all page of ebook, please click here login for view all page.
Reset
3.138.105.255