Creating Models of Real-World Communities with ReferralWebCreating Models of Real-World Communities with ReferralWeb

Henry KautzUniversity of Washington

Bart SelmanCornell University

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Recommender SystemsRecommender Systems

New category of software: programs that make personalized recommendations of goods, services, and people

• Amazon.com - books

• Jango.com - stores

• Whowhere.com - friends

Current methodsContent-based: find things similar to ones you like

Collaborative-filtering: find things liked by people who are similar to you

Explosive growth• Viewed as crucial for e-commerce sites

• Excite: 100,000,000+ recommendations per day!

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Anonymous OpinionsAnonymous Opinions

Most recommender systems hide the identity of the sources of the recommendations

• E-communities: fictitious identities

• Matchmaker systems: deliberately hide true identities

• Collaborative filtering: aggregation - no one to trust (or blame!)

Result: anonymous opinions• Okay choosing a movie or CD

• But would you bet your job on that “recommendation”?!

– Gee, boss, the project failed, but somebody on the net, I don't know who, said it was a good approach...

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Trusted RecommendationsTrusted Recommendations

For serious life / business decisions, you want the opinion of a trusted expert

• If an expert not personally known, then want to find a reference to one via a chain of friends and colleagues

Referral-chain provides:• Way to judge quality of expert's advice

• Reason for the expert to respond in a trustworthy manner

Finding good referral-chains is slow, time-consuming, but vital

• business gurus on “networking”

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Example TasksExample Tasks

• You are an associate editor for JAIR. Find a reviewer for a paper that claims new results on “expander graphs”.

• You are considering transferring to a different division of your company. Is that division head a good guy to work for?

• You are putting together a project team to launch a new internet service. Who in your company should you tap for expertise on image compression?

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ReferralWebReferralWeb

Set of all possible referral-chains = a social network

System for modeling, visualizing, and searching social networks

• in a company

• in an e-community

• in the WWW as a whole

Integrates IR search with a model of personal connections

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Social NetworksSocial Networks

Social network model specifies:• Who knows who

• Who knows what

How to create?• Ask users to register with system and provide lists

of contacts and interests– sixdegrees, 6DOS, Firefly, Whowhere?

• High startup cost

• Incomplete, out of date, untrustworthy information

• Best experts will actively avoid– a network of the lonely and disenfranchised?

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Mining Social NetworksMining Social Networks

Alternative: automatically generate network models from pre-existing data

• Email logs (not)

• Bibliographic databases

• Corporate records of organizational structure, project teams, in-house documents

• Arbitrary web pages– personal web pages more accurate / up to date than

official corporate records!

Can extract evidence for both relationships and expertise

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Discovering NamesDiscovering Names

Proper name extraction• Can accurately identify names in arbitrary

documents

• Frequency of co-occurrence of names can be quickly determined using IR search engines

Canonizing names• John Zack, J. C. Zack, Jim Zack

• Match names / initials / nicknames as long as unambiguous

– closed world assumption

• Improvement: use context– “Henry A. Kautz” matches “Harry A. Kautz”

if both strongly linked to “Bart Selman”

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Disambiguating NamesDisambiguating Names

Problem: different individuals with the same name

Observation: Within even large organizations the vast majority (90%+) of full names are unique

• 3,000 employees in R&D at AT&T

• 10,000 research scientists in AI, NL, and theory

For medium size networks - considered as noise• Key interface issue: ability to explain each link in

path to users

Further scaling: name + additional context

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User ProfilesUser Profiles

Manually-entered profiles incomplete, impossible to maintain

• impossible in principle to create complete a-priori list of kinds of expertise

Many services today create highly specialized profiles

• your book buying habits

Simple, robust profile: “bag of words” of all documents in which your name appears

• standard IR vector space model to match queries, people

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Test NetworksTest Networks

1. Proof of concept: 1,000 node network • Created by combination of web crawling and Altavista

queries, centered on a professor at M.I.T.

• Test group of users could usually find experts on given topics

– but small size of network led to distant referrals

2. 10,000 Researchers in AI, Theory, and NL• Based on 30,000 bibliography entries from high-

quality conferences– AAAI, STOC, FOCS, ACL...

• links between co-authors (not citations)

• http://www.research.att.com/kautz/referralweb

• “paper-reviewer finder”

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Exploring the NetworkExploring the Network

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Who can I ask to review a paper on “expander graphs”?Who can I ask to review a paper on “expander graphs”?

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Experts on Expander GraphsExperts on Expander Graphs

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Paths to ExpertsPaths to Experts

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Request Details on FriezeRequest Details on Frieze

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Frieze Home PageFrieze Home Page

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ObservationsObservations

Quickly found short chains to experts• Could not be found using IR search alone

User can select chain that is most likely to succeed

• Do not want to bother busiest, most famous experts with every request

Chains cross disciplines• Kautz - AI

• Kearns - AI, Machine Learning

• Blum - Machine Learning, Theory

• Frieze - Theory, Mathematics

Useful tool for strengthening ties both within and between communities

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Why Does it Work?Why Does it Work?

The Small World PhenomenaMilgram (1967) - any two individuals in the U.S.A. are

linked by a chain of 6 or fewer first-name acquaintances

– “6 degrees of separation”

– Erdös numbers

– “6 degrees of Kevin Bacon”

But• No formal model to explain short paths!

• Due to high average degree?

• True for acquaintances or co-stars, but false for our computer science co-author database!

– 100’s versus 61 versus 4.28!

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Small-world NetworksSmall-world Networks

Due to randomness?• Random graphs have short average path lengths

• But social networks are not random– nodes are highly clustered (many cliques)

– random graph model predicts that high clustering corresponds to long average paths!

Better model: Small-world networks• Idea: a highly structured (clustered) network with

just a few random links (Watts & Stogatz, 1998)

• Result: high clustering + short paths!

• Random edges correspond to shortcuts– direct relationships between people who primarily

participate in different sub-communities

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Small-world vs. Random NetworksSmall-world vs. Random Networks

Clustering Coefficient = Average value of C(n) over all nodes, where

2) of neighbors ofnumber (

) of neighborsbetween edges ofnumber ()(

n

nnC

Size Avg Avg Path ClusteringDegree Length Coeff.

CS Co-authors 8,070 4.28 7.9 0.72Random 1 8,070 4.28 6.4 0.072

Film Co-stars 225,000 61 3.65 0.79Random 2 225,000 61 2.99 0.00027

Neurons 282 14 2.66 0.28Random 3 282 14 2.25 0.05

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Corporate CommunitiesCorporate Communities

Finding good internal experts a strategic business problem

• “intellectual assets” worthless if not consulted!

AT&T: 170,000 employees, 3,000 in the R&D community

• How to build a project team?

• What R&D people to consult for a new business venture?

• What business people to contact about a new technological breakthrough?

In practice: successful projects based on grassroots cross-organizational networking

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Modeling the AT&T Corporate Network

Modeling the AT&T Corporate Network

Model integrates information from• Official organizational charts (online)

• Personal web pages (+ crawling)

• External publication databases

• Internal technical document databases

Informal structure will prove vital for• finding shorter paths to experts

• finding people who can reliably evaluate experts

• synergy between official and unofficial channels

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Who can tell me about the Director of Speech Processing research at AT&T?

Who can tell me about the Director of Speech Processing research at AT&T?

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Paths With All Link TypesPaths With All Link Types

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Filtering link typesFiltering link types

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Paths With Only Organizational LinksPaths With Only Organizational Links

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Paths With Only Web/Article LinksPaths With Only Web/Article Links

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ObservationsObservations

Official company hierarchy only a sparse subset of the corporate social network

Shortest (and often best) paths involve a combination of official and unofficial links

• Conditions for trust and evaluation may greatly differ

• Global social network is the union of many different kinds of sub-networks

Search greatly aided when user can choose different views of the network

+ types of edge

+ strength of edge

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Who can help out my project with some great image compression software?

Who can help out my project with some great image compression software?

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A Note on BelievabilityA Note on Believability

Observation: the recommendations made by (any) recommender system tend to be either astonishingly accurate, or absolutely ridiculous

• true for any AI-complete problem

How can a recommender system be trusted enough for “serious” use?

• Make system transparent: able to explain its reasoning

• indicate to user where the data is ambiguous

• Any link or node can be explained by viewing the data on which it is based

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Checking the Expert’s ExpertiseChecking the Expert’s Expertise

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Checking the Reason for an EdgeChecking the Reason for an Edge

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Verifying the Edge ContextVerifying the Edge Context

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SummarySummary

Many uses of recommender system require connecting people to people, not just providing “oracular” advice

• Find people, not just documents - access to information that may not even be online!

• Help users evaluate quality of information

Need to automatically model existing, real-world communities

• Cannot require everyone to sign up in advance!

• Can improve and strengthen the “weak ties” that are crucial for effective organizations

ReferralWeb: a tool for generating and searching social networks

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Status and Future WorkStatus and Future Work

ReferralWeb• Version 2.0 for the Computer Science research

communityhttp://www.research.att.com/~kautz/referralweb

• Corporate version undergoing trials in AT&T Labs

Current research topics• Automatic clustering - discovery of sub-

communities

• Combining uncertain information

• Scale-up to WWW-size communities

• Analysis of more accurate formal models of small-world networks

– accurately predict search performance

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BibliographyBibliography

• Kautz, H., Selman, B. & Shah, M. 1997. The Hidden Web. AI Magazine 18(2): 27-36.

• Milgram, S. 1967. The Small-World Problem. Psychology Today 1(1): 60-76.

• Resnick, P., ed. 1996. Special Section on Recommender Systems. Communications of the ACM 30(3).

• Watts, D. & Stogatz, S. 1998. Collective dynamics of ‘small-world’ networks. Nature 393: 440-442.