Dynamic Context-Sensitive PageRank for Expertise Mining

2nd Int. Conf. on Social Informatics (SocInfo'10)27-29 October, 2010, Austria

http://www.infosys.tuwien.ac.at/staff/dschall/29. Oct. 2010

Daniel Schallschall@infosys.tuwien.ac.at

Vienna University of Technology

Presentation Outline• Overview• Motivation• Human-Provided Services (HPS)

Crowdsourcing Example• Human Interaction Metrics• Dynamic Skill and Activity-based PageRank

(DSARank)• Experiments and Conclusion

2

• Open dynamic ecosystems– People and software services

integrated into evolving “solutions“• Communications and

coordination– „Anytime-anywhere“ pervasive

infrastructures and mobility• Mass collaboration

– Knowledge sharing and social interaction

• Crowdsourcing– Human computation on the Web

3

Overview Paradigm: human and service interactions

… software service

… user

… human/service interaction

• BPEL4People/WS-HT• User driven versus modeled

tasks in workflow

• Crowdsourcing• Human Intelligent Tasks (e.g., Amazon

Mechanical Turk)• No collaboration link

between humans

Motivation: Human computation/SOA

4

Modeling of human interactions in dynamic service-oriented systems

Reputation mechanism and expertise ranking in large-scale systems

Process flow Web services

People activity/human task

Knowledge sharing platform

Tasks

Requester

task1 task2

task4

task3

5

Definition

DiscoveryHPS

Interactions

Schall et al. (2008), Unifying Human and Software Services in Web-Scale Collaborations, IEEE Computer

Human Provided Service: Crowdsourcing Example

Overview Metrics

6

• Classification of MetricsSchall (2009), Human Interactions in Mixed Systems - Architecture, Protocols, and Algorithms (PhD Thesis)

Challenges

7

• How to find the most relevant expert?• How to calculate the expertise of people in an

automated manner?• How to account for changing interests and the

skill level in different fields of interest?My Approach• Dynamic Skill and Activity-based PageRank• Interaction mining using link-intensity weights• Personalization based on interaction context• Aggregated importance using query terms

8

• (1) Logging interactions• (2) Create interaction graph (offline)• (3) Aggregate ranking results based on preferences (online)

Discovery and Ranking

Expert Seeker (e.g., Crowdsourcing engine)

Schall (2009), Human Interactions in Mixed Systems - Architecture, Protocols, and Algorithms

Ranking Algorithm: Random surfer model

9

1/2 1/3

Web Graph

… node

… surfer

… Web link

With a certain probability, I will jump (“teleport”) to a random Web page.

Page et al. (1999), The PageRank Citation Ranking: Bringing Order to the Web.

NvoutlinksvPRuPR

uinlinksv

1)1(|)(|

)()()(

Ranking Algorithm: Behavior model

10

w1,2

Interaction Graph

… document

… user

… link

w1,31

3

2

5

4

6

I will contact User 2 depending on the link weight w1,2. The link weight is based on strength and intensities of interactions.

w2,4

I will contact some other user. For example, to start a new collaboration by relaying a message.

Ranking Algorithm: Interaction context

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• Users interact in different contexts with different intensities

12

context 1 (e.g., topic = WS Addressing)

1

context 2 (e.g., topic = WS Policy)

Interaction intensity context 1

Interaction intensity context 2

• Personalize ranking (i.e., expertise) for different contexts

Context-dependent DSARank

12

w1,2

Context 1

w1,31

3

2

4

w2,4

• (1) Identify context of interactions („tags“)

• (2) Select relevant links and people• (3) Create weighted subgraph (for

context)• (4) Perform mining

w1,3

Context 2

w1,41

4

3

User 1’s expertise in context 1

User 1’s expertise in context 2

)(...)()';( 11'

upwupwDSAwCuDSA nnCc

c

Calculated offlineE.g., p(u) = w1 IIL(u) + w2 availability(u)

Combined online based on preferences

Results

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• Real dataset (Email)• High interaction

intensity reveals key people

• Best informed usersID Rank (DSA) Rank (PR) Intensity Level

37 1 21 7.31...

253 4 170 2.07347 5 282 1.39

(see paper for detailed experiment results)

Conclusion

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• Crowdsourcing gains popularity• Amazon Mechanical Turk• Recognition from scientific community

• Human-Provided Services• Supporting versatile crowdsourcing scenarios

• Context-sensitive expertise• Important in collaborative crowd environments• Based on topic sensitive interaction mining

Thanks for your attention!

Daniel Schallschall@infosys.tuwien.ac.at

Vienna University of Technologyhttp://www.infosys.tuwien.ac.at/staff/dschall/

http://en.wikipedia.org/wiki/The_Turk