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Introducing the Userfeeds Protocol
An open protocol for establishing information
relevance in crypto-economic networks.
Why “Fake News” Is Relevant
@maciejolpinski
Research Development
Protocol + Software platform
development:

Userfeeds Relevance Platform

LinkExchange.io
Economics of attention &
information relevance:
Userfeeds Protocol
What We Do?
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
It didn’t 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?
What is relevant information?
Naftali Tishby
Fernando C. Pereira
William Bialek
The Information
Bottleneck method
https://arxiv.org/abs/physics/0004057
“We define the relevant information in a signal x ∈ X as
being the information that this signal provides about
another signal y ∈ Y .”
Source: The Information
Bottleneck method paper https://arxiv.org/abs/physics/0004057
“Examples 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.”
Source: The Information
Bottleneck method paper https://arxiv.org/abs/physics/0004057
Mike
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 “relevance” on the Web:
Increasing relevance on the Internet is all about
pursuing the ability to elicit “predictable”
behaviours in others.
“Fake news”, digital addiction are natural consequences of
companies seeking more relevance / predictability.
Single “Evaluation” is an Atomic Signal of
Relevance in Networks
Website
Website
Person
Photo
Bank Account Purchase
Like
Link
“Anchor Text”
Product
Aggregated evaluations can be
“compressed” by algorithms to
extract relevance (predictability)
Platform
Google
Facebook
Amazon
Context
Keyword
Social Circle
Consumer
Vertical Relevance
Subjective
Context Dependent

Individual

Feeds

‘Silicon Valley’ business model

Personalization Algorithms

In Space

Likes, Links, Followers
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
Horizontal Relevance
Objective
Context Independent

Shared

Ledgers

‘Wall Street’ business model

Consensus Algorithms

In Time

Money, Assets
$ $ $ $ $ $ $ $ $ $ $
loss of
information
during
transitions
Silicon Valley
Data Silo
Wall Street
Data Silo
You are here
You are here
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.
$ $ $ $ $ $ $ $ $ $ $ $ $ $ $ $ $ $ $ $ $ $ $ $ $ $ $ $ $
Horizontal
Relevance
Vertical
Relevance
Token
Token Price
Token
Token Price
Token
Token Price
Tokenisation
?
Question: “how to go back from consensus to personalization?”
“Proof of evaluation” 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
One message type
for all evaluations.
Like
Photo
Link
URL
“anchor text”
Token Transfer
BlockchainAddress
Cryptographic Key
Ranking API
Application
Ranking AlgorithmRanking Algorithm
IPFSEthereum
Blockchain
Amazon S3
Evaluation
Display
Rankings
Aggregation
Publication
Protocol
Layers
Aggregated
Graph of
‘Proofs of
Evaluation’ ImageTextVideo
Infinite 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
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
Today
Proprietary and Separated
Evaluation Graphs

In Space
In Time
InSpace
In Time
Userfeeds Protocol
Open and Integrated

Evaluation Graphs
Personalization
Consensus
Userfeeds Protocol
Relevance That Combines
Consensus With
Personalization
Use Cases
It’s 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
18 3.9212
Boost
5 In slot
15%
Pay by Metamask33.0001 E T H
0% 100%
72.3%
Available: 15.032 ETH
LinkExchange.io
Token holders
Publishers
Advertisers
Link Exchange for Personal ERC20 Tokens
"Why Fake News Is Relevant" - Introduction to the Userfeeds Protocol
"Why Fake News Is Relevant" - Introduction to the Userfeeds Protocol
… and it works with Augmented / Mixed Reality too
cryptopurr.co
“Social Network” for Cats
… or any ERC721 token
"Why Fake News Is Relevant" - Introduction to the Userfeeds Protocol
"Why Fake News Is Relevant" - Introduction to the Userfeeds Protocol
State Of The Dapps & LinkExchange - coming soon
Promote dApps with Ether
"Why Fake News Is Relevant" - Introduction to the Userfeeds Protocol
Thank You
@userfeedsuserfeeds.io
@maciejolpinski

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"Why Fake News Is Relevant" - Introduction to the Userfeeds Protocol