This document introduces the Userfeeds Protocol, an open protocol for establishing information relevance in crypto-economic networks. It discusses the problems with how information relevance is currently solved by centralized platforms and proposes using a decentralized protocol and blockchain to aggregate evaluations across contexts to extract both vertical and horizontal relevance. This would allow tokenization of relevance domains and open up new opportunities for consensus-based and personalized relevance. The current implementation includes a Userfeeds Relevance Platform built on Ethereum with ranking algorithms and interfaces. Several potential use cases are described, including a Link Exchange platform to monetize token-based communities and applications.
"Why Fake News Is Relevant" - Introduction to the Userfeeds Protocol
1. Introducing the Userfeeds Protocol
An open protocol for establishing information
relevance in crypto-economic networks.
Why âFake Newsâ Is Relevant
@maciejolpinski
2. Research Development
Protocol + Software platform
development:
Userfeeds Relevance Platform
LinkExchange.io
Economics of attention &
information relevance:
Userfeeds Protocol
What We Do?
3. What happened in 2017
Seed Funding
Initial Idea + Prototype
Platform + Apps Live
Userfeeds Relevance Platform
Link Exchange
Team Ramped Up to 7 People
(5 Software Engineers + 2 Founders)
January 2017 January 2018
4. It didnât solve the problem of
information relevance.
HTTP/TCP/IP/SMTP solved the problem
of information transmission.
Relevance is solved by the application
layer companies today.
The Problem
How to solve the relevance problem
for the Web 3.0 stack?
6. Naftali Tishby
Fernando C. Pereira
William Bialek
The Information
Bottleneck method
https://arxiv.org/abs/physics/0004057
7. âWe deïŹne the relevant information in a signal x â X as
being the information that this signal provides about
another signal y â Y .â
Source: The Information
Bottleneck method paper https://arxiv.org/abs/physics/0004057
8. âExamples include the information that face images provide about the names
of the people portrayed, or the information that speech sounds provide about
the words spoken.â
Source: The Information
Bottleneck method paper https://arxiv.org/abs/physics/0004057
Mike
9. Relevant information in behaviours (x) of people
on the Web (X) is the information that it provides
about other behaviours (y) of other people on
the Web. (Y)
What if we could extrapolate this definition to the broader
definition of ârelevanceâ on the Web:
10. Increasing relevance on the Internet is all about
pursuing the ability to elicit âpredictableâ
behaviours in others.
âFake newsâ, digital addiction are natural consequences of
companies seeking more relevance / predictability.
11. Single âEvaluationâ is an Atomic Signal of
Relevance in Networks
Website
Website
Person
Photo
Bank Account Purchase
Like
Link
âAnchor Textâ
Product
12. Aggregated evaluations can be
âcompressedâ by algorithms to
extract relevance (predictability)
Platform
Google
Facebook
Amazon
Context
Keyword
Social Circle
Consumer
14. selling
advertising
product
The aggregator business model of the Web 1.0 and Web 2.0
Extract relevance from as many contexts as possible and
convert to money
buying
advertising
product
$ $ $ $ $ $ $ $ $ $ $ $cash
stock price
16. $ $ $ $ $ $ $ $ $ $ $
loss of
information
during
transitions
Silicon Valley
Data Silo
Wall Street
Data Silo
You are here
You are here
17. Internet 3.0 will create an explosion of horizontal relevance
domains via tokenization. Each of them will be individually
evaluated by the market.
This will open up a new, temporal axis for establishing future relevance
of ideas and information.
$ $ $ $ $ $ $ $ $ $ $ $ $ $ $ $ $ $ $ $ $ $ $ $ $ $ $ $ $
19. âProof of evaluationâ is a basic signal of relevance.
One Message Type for Both Vertical and Horizontal Relevance
A Bmetadata C
Cryptographic Key Arbitrary StringArbitrary Metadata
Userfeeds protocol
20. One message type
for all evaluations.
Like
Photo
Link
URL
âanchor textâ
Token Transfer
BlockchainAddress
Cryptographic Key
23. InïŹnite Evaluation Cycle
Relevance Layer as Integral Part
of the Web 3.0 stack Ranking API
Ranking API
Observer
Application
Observer
Ethereum
Blockchain
IPFS
Amazon S3
Amazon S3
Ranking Algorithm
Ranking Algorithm
Ranking Algorithm
Ranking Algorithm
IPFS
Ethereum
Blockchain
Amazon S3
Application
24. Data layer
Processing layer
Display layer
Current Implementation of The Protocol
Userfeeds Relevance Platform
Ethereum Blockchain
X
Database
AlgorithmsAlgorithms
API
InterfaceInterfaceInterface
Userfeeds Relevance Platform
Blockchain
Y
29. Itâs like Google AdSense for
Token-based Communities
Link Exchange is Coming Soon
Click on an ETH icon to see an early preview
https://userfeeds.io
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