Social Networks & e-Business: Capturing Real-Time Niche Markets

©OpenProcess, Inc. Do not copy. Do not distribute. Mental Distance, 1

Mental Distanceand Its Implications for the Design of Software and Data

David A. Marca

University of PhoenixOne Research Drive

Westborough, Mass 01581U.S.A.

dmarca@email.phoenix.edu

July 24, 2010

Welcome!5th

International ConferenceSoftware and Data Technologies

ICSOFT 2010, Athens, Greece, 22-24 July

Mental Distanceand Its Implications for the Design of Software and Data

David A. Marca

University of PhoenixOne Research Drive

Westborogh, Mass 01581U.S.A.

July 24, 2010

Important Note• Case study is: www.dooce.com.

• It is an excellent social network.

One of the best for showing the . traditional perspective of design.

The author wishes www.dooce.com

. much continued success!

DEFINITION Mental Distance

Mental distance is a measure of the conceptual similarity between the underlying intention of:

1) a social network conversation2) an online ad.

Long distance = poor ad alignment.

Short distance = good ad alignment.

Chen, 2002Lewis, 2003

THESIS Language-Action Perspective

Given this definition of mental distance…the language-action perspective can give business:

1) access to small, dynamic niche markets occurring in social networks,

2) a way to create highly aligned online ads for those markets.

Marca, 2010©OpenProcess, Inc. Do not copy. Do not distribute. Mental Distance, 4

Mental Distanceand Its Implications for the Design of Software and Data……

Part 1 – Prolog●

Social Networks

Part 1 – PrologPart 2 – Traditional Perspective

Populations●

Connections

Conversations●

Long Distance

Part 1 – PrologPart 2 – Traditional PerspectivePart 3 – Alternate Perspective

Inference●

Intention

Clusters●

Short Distance

Part 1 – PrologPart 2 – Traditional PerspectivePart 3 – Alternate PerspectivePart 4 – Software Implications

Architecture●

Ontology

History●

Agents

Part 1 – PrologPart 2 – Traditional PerspectivePart 3 – Alternate PerspectivePart 4 – Software ImplicationsPart 5 – Epilog

Semantic Web Fit

Part 1 – Prolog●

Social Networks

1. Social Networks Some Early Social Network Sites

Kasavanna, 2009

1. Social Networks Exponential Growth 2003 to 2008

Cha, 2009

Part 1 – PrologPart 2 – Traditional Perspective

Populations●

Connections

Conversations●

Long Distance

2. Populations Facebook = 4th Largest Population in the World

Scale, 2008

This week, just surpassed 500M users:http://www.bbc.co.uk/news/technology-10713199

2. Populations Case Study: www.dooce.com … affluent U.S. Mothers

January 4, 2010

A monolog onwork-life balance.

3. Connections Typical Structures, Complex, Hard to Analyze

Core –

Periphery Cliques

Scale FreeWatts-Strogatz

(friends)Freeman, 2000

3. Connections Example: Twitter “Followers” Network

A Twitter “Followers”

Relationship Network

Hopkin, 2010

3. Connections Example: Twitter “Following” Network

A Twitter “Following”

A Twitter “Followers”

Hopkin, 2010

4. Conversations Example: Twitter “Conversation” Network

A Twitter “Following”

A Twitter Conversation NetworkA Twitter “Followers”

Hopkin, 2010

4. Conversations Case Study: Diverse Family Life Conversations

An early morninglecture in semantics

“Can I have my treat after I finish myCocoa Puffs?”

“I think we need to have a long talkabout what youdefine as a treat.”

face hiddento protectprivacy

January 4, 2010

4. Conversations Hard to Classify: Many, Varied Conversations

Breakfast

Journals

Exercise

Fatherhood

January 4, 2010

4. Conversations Simple Search Organization? ... May Not Work

Breakfastis missing(word not

in title)

January 4, 2010

5. Long Distance Generalize Profile Poor Ad Alignment …

Laudon, 2004

5. Long Distance Case Study: Ads Match Profile, Not Conversation

Hair Spray

January 4, 2010

A monolog onwork-life balance.

5. Long Distance Generalize Segment Poor Ad Alignment …

Laudon, 2004

5. Long Distance Case Study: Ads Not Aligned to Breakfast

Hair Spray

January 4, 2010

5. Long Distance Case Study: One Breakfast Ad … Poor Alignment

Hair Spray

January 4, 2010

Part 1 – PrologPart 2 – Traditional PerspectivePart 3 – Alternate Perspective

Inference●

Intention

Clusters●

Short Distance

6. Inference Case Study: Here is a conversation…

“Can I have my treat after I finish myCocoa Puffs?”

January 4, 2010

6. Inference Natural Language Inference Engine Concepts

“Can I have my treatafter I finish myCocoa Puffs?”

wantstreat

Horvitz, 1999

6. Inference Product Knowledge Inference Engine Things

wantstreat

hadcereal

Ghani, 2002

6. Inference Word Associations Inference Engine Meaning

wantstreat

hadcereal

serioustalk

Andrews, 2005

6. Inference Social Context Inference Engine Meaning

wantstreat

hadcereal

wantssugar

hadsugar

serioustalk

Ehrlich, 2007

6. Inference Intention Ontology Inference Engine Intent

wantstreat

hadcereal

wantssugar

hadsugar

serioustalk

stopsugar

HealthyChild

Banks, 2002

7. Intention Three Types…

Heidegger, 1962

7. Intention Intention for Being, Declaration: say who you are

HealthyChild

Self Backstrom, 2006

7. Intention Intention for Acting, Goal-oriented: do what you say

HealthyChild

CorrectDiet

Acting

Others Sabatar, 2002

7. Intention Intention for Having, Possession-oriented: get results

HealthyChild

CorrectDiet

HealthyCereal

Being Acting Having

Self Others Things Recker, 2005

7. Intention Intention for Being is context for Intention for Acting

HealthyChild

CorrectDiet

HealthyCereal

Being Acting Having

Self Others ThingsHeidegger, 1962

7. Intention Intention for Acting is context for Intention for Having

HealthyChild

CorrectDiet

HealthyCereal

Being Acting Having

Self Others ThingsHeidegger, 1962

8. Clusters A Conversation Cluster Creates an Intention

Bergstrom, 2009

8. Clusters Conversation 1: Articulating Need

Cohen, 2002

8. Clusters Conversation 2: Forming “Right” Relationships

Relationship

Wang, 2004

8. Clusters Conversation 3: Creating Possibilities

Relationship

Possibility

Denning, 2003

8. Clusters Conversation 4: Determining Opportunities

Relationship

Possibility

Opportunity

Denning, 2003

8. Clusters Conversation 5: Making Decisions

Relationship

Possibility

Opportunity

Decision

Dougherty, 1992

8. Clusters Conversation 6: Taking Appropriate Action

PossibilityDecision

Opportunity

Action Relationship

Winograd, 1998

HealthyChild

9. Short Distance Intention Local Bias Niche Personalization

Laudon, 2004

9. Short Distance #1. Declare an Intention for Being to Social Network

PossibilityDecision

Opportunity

Action Relationship

Being Acting Having

Self Others Things

HealthyChild

Backstrom, 2006

9. Short Distance #2. Group Agrees Upon an Intention for Acting

PossibilityDecision

Opportunity

Action Relationship

Being Acting Having

Self Others Things

HealthyChild

CorrectDiet

Hill, 2006

9. Short Distance #3. Group Agrees Upon an Intention for Having

PossibilityDecision

Opportunity

Action Relationship

Being Acting Having

Self Others Things

HealthyChild

CorrectDiet

HealthyCereal

Praise, 2008

9. Short Distance (Note Match with Classical Marketing-Sales Process)

FunctionFeaturePurchase

Preferences

Loyalty Awareness

Being Acting Having

Self Others Things

HealthyChild

CorrectDiet

HealthyCereal

Ren, 2003

9. Short Distance #4. Group Agrees Upon Product Preferences

Being Acting Having

Self Others Things

HealthyChild

CorrectDiet

nutrients

nosugar

willeat

HealthyCereal

Kamaladevi, 2010

9. Short Distance #5. Products are Matched to Preferences

nutrients

nosugar

willeat. . .

Fiss, 2007

9. Short Distance #6. Ad Alignment: Consistency, Relevance, Branding

Cereal

No MovieNo Books

January 4, 2010

Bundle

Part 1 – PrologPart 2 – Traditional PerspectivePart 3 – Alternate PerspectivePart 4 – Software Implications

Architecture●

Ontology

History●

Agents

10. Architecture #1. Parse: Conversation Word Neighborhoods

Natural LanguageProcessing

Conversation

Phrases

Mayfield, 2003

10. Architecture #2. Infer: Word Neighborhoods Possible Intent

Inference Engine:Intention

Conversation

Phrases

Possible Intentions

Lee, 2001

10. Architecture #3. Infer: Possible Intent Verified (Shared) Intent

Analysis: ConfirmShared Intent

Conversation

Phrases

Possible Intentions

Verified Intention

Paek, 2000

10. Architecture #4. Tag: Verified (Shared) Intent Niche Market

Inference Engine:Niche Market

Conversation

Phrases

Possible

Intentions

Verified Intention

Hunscher, 2006

10. Architecture #5. Connect: Niche Market Preferences Ads

Inference Engine:Preferences Ads

Conversation

Phrases

Possible Intentions

Verified Intention

Aligned Ads

Hogg, 2009

10. Architecture #6. Ad Click Collective Bargaining Purchase

Agent: CollectiveBargaining

Conversation

Phrases

Possible Intentions

Verified Intention

Aligned Ads

Purchase Lin, 2010

11. Ontology “Conversation” and Its Types + Their Elements

Conversation

Phrases

Possible Intentions

Verified Intention

Aligned Ads

Purchase

ConversationOntology TYPES:

Need●

Relationship●

Possibility●

Opportunity●

Decision●

ACTION

ELEMENTS:●

Request●

Promise●

Offer●

Counter●

Weigand, 2008

11. Ontology “Intention” and Its Types + Elements (for e-Business)

Conversation

Phrases

Possible Intentions

Verified Intention

Aligned Ads

Purchase

IntentionOntology

ConversationOntology TYPES:

Being●

Doing●

HAVING

ELEMENTS:●

Intent to Buy●

Product●

Preferences●

Order●

Pricing●

Barbosa, 2005

11. Ontology Shared Intent, Agreement, Grounded Understanding

Conversation

Phrases

Possible Intentions

Verified Intention

Aligned Ads

Purchase

InteractionOntology

IntentionOntology

ConversationOntology DIMENSIONS:

Intent●

Agreement●

UNDERSTANDING

ELEMENTS:●

Utterance●

Presentation●

Acceptance●

Confirmation●

Paek, 2000

See also Milo demonstration at:http://www.bbc.co.uk/news/10623423

11. Ontology Marketing Mix Approach Elements are Intention-based

Conversation

Phrases

Possible Intentions

Verified Intention

A;igned

Purchase

Niche MarketOntology

InteractionOntology

IntentionOntology

ConversationOntology MARKETING MIX:

Product●

Pricing●

Distribution●

COMMUNICATION

ELEMENTS:●

Agents

(understand…each niche market’s…intentions)●

Bundle

(built for one… intended

use)●

(High Alignment)●

Brand Equity…(product function =… intended use)●

Engelbach, 2006

11. Ontology Content-based Access using Intentions + Distances

Conversation

Phrases

Possible Intentions

Verified Intention

Aligned Ads

Purchase

Online AdOntology

InteractionOntology

IntentionOntology

ConversationOntology ACCESS TO:

Product Catalogs●

Yellow Pages

LIMITED BY:●

Constant Updates●

Rigid Set of Terms●

Large Vocabulary

SOLUTION:●

Vocabulary:…terms

= Intentions●

Relationships:…semantics among …terms = distances …between intentions

Guarino, 1999

11. Ontology Intentions = Non-Ambiguous Preferences Mapping

Conversation

Phrases

Possible Intentions

Verified Intention

Aligned Ads

Purchase

NegotiationOntology

Online AdOntology

InteractionOntology

IntentionOntology

ConversationOntology MAPPING:

Buyer preferences●

Seller preferences●

Intention-based map

NEGOTIATION:●

Non-ambiguity●

Use intentions in the …Yellow Pages and…Catalogs

BEYOND PRICE:●

Satisfy Need…●

Satisfy Intended Use

Beam, 1996

12. History Reuse: Words Neighborhoods Intentions

Conversation

Phrases

Possible Intentions

Verified Intention

Aligned Ads

Purchase

WordNeighborhoods

NegotiationOntology

Online AdOntology

InteractionOntology

IntentionOntology

ConversationOntology

Craven, 2000

WORDS…●

Assumptions●

Inter-relationships●

Collections

CONTEXT:●

Specific Topic●

The Conversation

CAUTION:●

Generic Associations●

Prior attempts to use…those assumptions…without success.

12. History Reuse: Contexts + Phrases Intentions

Conversation

Phrases

Possible Intentions

Verified Intention

Aligned Ads

Purchase

WordNeighborhoods

IntentionNetworks

NegotiationOntology

Online AdOntology

InteractionOntology

IntentionOntology

Paek, 1999

Context = Breakfast

1) Treat Sugar

2) No Treat No Sugar

3) No Sugar Health

CAUTION:Reasoning under uncertainty is likely.

12. History Build: Self-Organizing Map of “Similarity Distances”

Conversation

Phrases

Possible Intentions

Verified Intention

Aligned Ads

Purchase

WordNeighborhoods

IntentionNetworks

IntentionDistances

NegotiationOntology

Online AdOntology

InteractionOntology

IntentionOntology

Chen, 2002

HEALTH01. WELL-BEING

03. WELLNESS

07. VITALITY

12. FITNESS

18. STRENGTH

12. History Use: Intention (Short Distance) More Preferences

Conversation

Phrases

Possible Intentions

Verified Intention

Aligned Ads

Purchase

WordNeighborhoods

IntentionNetworks

IntentionDistances

NicheMarkets

NegotiationOntology

Online AdOntology

InteractionOntology

IntentionOntology

ConversationOntology HEALTH

Nutrients-

No Sugar-

Will Eat

HEALTH

01. WELL-BEING-

Vitamins-

Omega-3

Cohen, 2000

12. History Organize: Taxonomy + Configuration + Qualities

Conversation

Phrases

Possible Intentions

Verified Intention

Aligned Ads

Purchase

WordNeighborhoods

IntentionNetworks

IntentionDistances

NicheMarkets

ProductCatalogs

NegotiationOntology

Online AdOntology

InteractionOntology

IntentionOntology

Brafman, 2007Buchner, 1999

CEREAL

- Nutrients- No Sugar

- Nutrients- No Sugar- Small Size

- Nutrients- No Sugar- Small Size- Will Eat

12. History Negotiation Success + Strategic Delay Low Cost

Conversation

Phrases

Possible Intentions

Verified Intention

Aligned Ads

Purchase

WordNeighborhoods

IntentionNetworks

IntentionDistances

NicheMarkets

ProductCatalogs

PurchaseNegotiations

NegotiationOntology

Online AdOntology

InteractionOntology

IntentionOntology

Mok, 2005Winoto, 2002

STRATEGIC DELAY

©OpenProcess, Inc.

13. Agents Hybrid = Knowledge Base + Collaborative Filtering

Internet Extranet Intranet Information

SocialNetwork

ProductCompany

Tran, 2000

Mental Distance, 76

13. Agents Knowledge Base: Standard Taxonomy, All Configs.

SocialNetwork

ProductCompany

PrivateCatalog

PublicCatalog

ConfigurationExperts

CIAgent

Public Offering

Guttman, 1998

Mental Distance, 77

13. Agents Knowledge Base: Similar Products + Configurations

SocialNetwork

ProductCompany

PrivateCatalog

PublicCatalog

CIAgent

Public Offering

Recommendation

CompetitiveInformation

Tran, 2000

13. Agents Collaborative Filtering: Intention-based Preferences

SocialNetwork

ProductCompany

PublicCatalog

IntentionAgent

Online Ads

PrivateCatalog

CIAgent

Public Offering

Recommendation

Preferences

Public Offering

Ravi, 2010

13. Agents Buy Step 1: Ad Conversation for Having Prefs.

SocialNetwork

ProductCompany

PrivateCatalog

PublicCatalog

NicheAgent

IntentionAgent

CIAgent

Public Offering

Intention +Preferences

AdClick

Recommendation

Online Ads

Preferences

Public Offering

Varian, 2010

13. Agents Buy Step 2: Preferences Vendors + Their Channels

SocialNetwork

ProductCompany

PrivateCatalog

PublicCatalog

NicheAgent

IntentionAgent

CIAgent

Public Offering

AdClick

CompanyAgent

ChannelAgent

Selection

Recommendation

Online Ads

Preferences

Public Offering

Pavlou, 2006

13. Agents Buy Step 3: Use Successful Negotiation Strategies

SocialNetwork

ProductCompany

PrivateCatalog

PublicCatalog

NicheAgent

IntentionAgent

CIAgent

Public Offering

AdClick

CompanyAgent

ChannelAgent

Bargaining

Recommendation

Online Ads

Preferences

Public Offering

Successful Strategies

Lee, 1998

13. Agents Buy Step 4: New Configurations Might Be Offered

SocialNetwork

ProductCompany

PrivateCatalog

PublicCatalog

NicheAgent

IntentionAgent

CIAgent

Public Offering

AdClick

CompanyAgent

ChannelAgent

Bargaining

Recommendation

Online Ads

Preferences

Public Offering

Private Offering

Configuration Need

Kim, 2008

13. Agents Buy Step 5: Publish Previously Non-public Configs.

SocialNetwork

ProductCompany

Recommendation

Public Offering

PublicCatalog

NicheAgent

IntentionAgent

AdClick

PrivateOfferings

CIAgent

CompanyAgent

ChannelAgent

Bargaining

PrivateCatalog

Online Ads

Preferences

Private Offering

Configuration Need

Public Offering

Dubrovsky, 1991

Part 1 – PrologPart 2 – Traditional PerspectivePart 3 – Alternate PerspectivePart 4 – Software ImplicationsPart 5 – Epilog

Semantic Web Fit

14. Semantic Web Fit Message + Transport Layers

HTTP, FTP, SMTP, …

WS Security Secure Messaging

XML Messaging

Transport

Web Services Stack

Yu, 2008

14. Semantic Web Fit Semantic-driven Computation: Intention + Distance

WS Security Secure Messaging

XML Messaging

Transport1.

Web Services Stack Extensions

Bussler, 2002

14. Semantic Web Fit Auto Service Composition: Catalogs + Yellow Pages

WS Security

WSDL Service Description

Secure Messaging

XML Messaging

Transport1.

2. Catalog of Configurations

UDDI Service Publication 2. Product Yellow Pages

Kajan, 2004

Semantic Web Fit Ontology-driven Profiling: Intention disambiguation niche preferences pinpoint ads simple discovery.

WS Security

UDDI / WS Inspection Service Discovery

Service Publication

Service Description

Secure Messaging

XML Messaging

Transport1.

3. Highly Targeted Ads

2. Product Yellow Pages

Parkhomenko, 2003

Semantic Web Fit Semantic Composability: Intention-based Configurations

Service Composition / Flow

WS Security

UDDI / WS Inspection

Trading Partner Agreement

Service Agreement

Service Discovery

Service Publication

Service Description

Secure Messaging

XML Messaging

Transport

Ordinary Transact / Deliver

tics 5. Agent-based Bargaining

3. Highly Targeted Ads

2. Product Yellow Pages

Medjahed, 2003

Thank you!LinkedIn

dmarca@openprocess.com

Mental Distanceand Its Implications for e-Business Software

David A. Marca

University of Phoenix2310 Crossroads Drive

Madison, WI 53718U.S.A.

July 24, 2010

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