The Future of Search EnginesTake it personally!

Emil IsmalonCo-founder and CTO

Collarity, Inc.

Internet 2008February 25, 2008

2008אינטרנט

2The Wisdom of Your Crowd

PhysicsPhysics BuddhistBuddhist meditation meditation

Quantum Quantum Holistic approachHolistic approach measurement problemsmeasurement problems

Machine learningMachine learning cognition & realitycognition & reality

ZenZen

The forceThe force AAbbstractions stractions

LoveLove information retrieval information retrieval mathmath MindMind & & matter matter LiaLia NoaNoa COLLABORATIONCOLLABORATION

Multidimensional Multidimensional clusteringclustering basketball basketball

Social networkingSocial networking how? how? DRUMS DRUMS

CollarityCollarity high frequency lasers high frequency lasers Swimming Swimming dear mamadear mama

My Tag cloud

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Beyond the Limits of Keyword Search

Amount of data

Pro

duct

ivity

of S

earc

h

Databases

Web 1.0 Web 1.0 1990 - 2000

The World Wide Web

PC EraPC Era1980 - 1990

The Desktop Keyword search

Directories

2000 - 2010Web 2.0 Web 2.0

The Social Web

Files & Folders

Tagging

Naturallanguage

search

2010 - 2020

Web 3.0 Web 3.0 The Semantic Web

Automated Content Analysis

2010 - 2020

Web 3.0 Web 3.0 User Modeling

Universal/Open IDUser profiling

** From: Making Sense of the Semantic Web, BY Nova Spivack

Syndicated WebIntelligent Web

Web 4.0 Web 4.0

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The Next Steps

Methods

Automated Content Analysis

Latent Space construction

(Augmented LSA LDA methods)

Semantic Web

Today’s ContentSocial networking

Media consumption

Commercial consumption

User ModelingUniversal/Open ID

User Profiling

Behavioral & Intentional Targeting

User interactions with content

TechnologicalEnablement

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Projection into the future

1. Better rankings-relevant results

2. Recommendation engine

3. Deep personalization

4.Digidentity

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DigidentityDigidentity

Digital Identity Entity

Shabti statues

“What I’m about”

My digital imprint

Independent Entity

AvatarUser

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PERSONALIZATION

Key Element:

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The pros & cons of personalization

Pros:• This is how the “real world” behaves: intents and

meanings can’ t usually described by a query• It is the natural way to achieve higher relevance and

enhanced user experience• Think about web navigation in 5-10 years from now..

Do you really think intelligent personal web agents will not emerge?

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Cons:• Lockup problems • Cold start - it takes time and education• Is there a general solution to personalization or is

it strongly dependent on contexts • Privacy ethics• Manipulations

The pros & cons of personalization

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Gradual perception of personalization

• Language preferences

• Geo targeting

• Simple Lingual Disambiguation The case of Java; is it programming OR island OR coffee?

• Social networking

• Form of preferable media Textual OR visual? Summary OR deep analysis?

== ==

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• Understanding the right perception while interacting: Pro OR anti Believer OR skeptical

• Subtle ones Quantum physics article most suitable to a biologist

Gradual perception of personalization

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bush

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bush

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Java

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User modeling

Eleanor Rigby’s recent queries:

Paul is dead

Did we land on the moon?

Elvis is alive!

Who murdered JFK?

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ABSTRUCTION

Collarity live example of conspiracy:

Moon Elvis JFK

http://www.collarity.com/search?query=Moon+Elvis+JFK

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Natural Audience Segmentation

Before: Traffic

• What are my important segments doing?• What are their likes/dislikes?• Are they finding what they’re looking for?• How are their tastes changing?

After: Natural Communities

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WAR – what is it good for?Augmented Personalization in Action

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The ability to personalizeIn order to materialize the subtle level of personalization an holistic approach is needed!

understand the user from many different aspects:

•Implicit Informational networking

•SegmentationCluster users in information space

•Topic based communities Clustering users around topics

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Space & Beyond

•Establishing the “Space”• Live metric - relevancy coordinates (attributes) • Embedding users profile

•Space dynamics• Predictions – Segmentation, Clustering

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Future of DigidentityFuture of DigidentityDigidentities Spontaneously Behave

User

TVTV

NewsNews WorkWork

MusicMusic HobbiesHobbies

InterestsInterestsKnowledgeKnowledge

SocialSocial

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Conclusions and Summary

1. Personalization involves “intelligence” characteristic; abstraction, generalization, predictive clustering

2. Users implicit informational communities can serve as a natural enhancement of user searching and browsing modeling

3. Enable Users to control personalization features and variables

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Further readings

• Pros & cones personalized search http://searchengineland.com/070309-081324.php

• Dynamic Social Network Analysis using Latent Space Modelshttp://books.nips.cc/papers/files/nips18/NIPS2005_0724.pdf

• Constraint-based Personalization Model: Multi-Channel Messaginghttp://www.research.att.com/~rjana/TothNagboth.pdf

• A Hybrid Web Personalization Model. Based on Site Connectivity.• Miki Nakagawa, Bamshad Mobasher ... on non-sequential models, such as

association rules and ...http://maya.cs.depaul.edu/~mobasher/papers/NM03b.pdf

• Social Balance Theory• Revisiting Heider’s Balance Theory for many agents

http://www.au.af.mil/au/awc/awcgate/lanl/social_balance_0405041.pdf