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Collaborative Filtering:Possibilities for Digital

Libraries

Collaborative Filtering:Possibilities for Digital

Libraries

Jon HerlockerJanet WebsterSeikyung Jung

Oregon State University

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Current search engines are insufficient.

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Two important search engine problems

Two important search engine problems

•They don’t understand:– Quality– Context

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But First: Our Context

But First: Our Context

•Why are we standing up here?

•We think we can improve the digital library experience.

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Today’s ContextToday’s Context

1. Research questions & hypotheses

2. Collaborative filtering 3. Our approach to CF in the

Library4. Challenges of collaborative

filtering for library search5. Initial lessons learned

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The Librarian’s Questions

The Librarian’s Questions

• As electronic information increases in amount and value, how to provide access to it?

• How to change digital libraries from disconnected collections to integrated systems?

• How to integrate the expertise of librarians into the development process?

• How to adapt traditional library values to new opportunities?

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The Computer Scientist’s Questions

The Computer Scientist’s Questions

• What is the next big leap in document search technology?

• How to overcome the limitations of software’s ability to understand language?

• How can we build a search engine that learns by observing searchers?

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Our Research Hypotheses

Our Research Hypotheses

• Enabling the entire community to participate in organizing and recommending information will add value to the digital library

•  In other words: Collaborative Filtering will increase the value of a digital library

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What is Collaborative Filtering?

What is Collaborative Filtering?

• Communities of people sharing their evaluations of content

• Recommendations are transferred between people of like interest

• Examples:– MovieLens.org– Epinions.com– Launchcast (launch.yahoo.com)– Amazon.com

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CF and LibrariesCF and Libraries

• Search is central to user experience of digital library

• Collaborative Filtering:– Could overcome the limitations of

current search technology– CF already exists in libraries.

• Not search, but cataloguing (OCLC)

• Adapting CF for document searching is not trivial.– Information needs are dynamic.

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Our ApproachOur Approach• OSU Libraries Recommender

System– Perform at CF at query level

• Match similar queries in addition to similar users

– Generate results based on past user recommendations

– Infer recommendations from user behavior

– Integrate with existing library systems and traditions

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The Benefits of CFThe Benefits of CF

• Quality is considered.– Recommendations are based

on human evaluations.• Context is considered.• The system gets better as

it’s used.• Doesn’t require significant,

centralized human resources

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CS ChallengesCS Challenges

• How to collect evaluations?• How to identify the “useful”

element of recommendations?• How to represent the

information needs of searchers?• How to rank results?• How to design the interface?

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Library ChallengesLibrary Challenges

• How to balance privacy with personalization & involvement?

• How to maintain authority of recommended information?

• How to deal with timeliness of information?

• How to integrate with existing library systems?

• How to fund research in the library setting?

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What We’ve Learned

What We’ve Learned

• Weakness of “old” search technology affects perception of new

• Wrapper technology minimizes IT commitment

• Existing internal data can be used to jumpstart system

• Controlled experiments show– Increased performance– Increased perception of non-

tangibles

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CF and Digital Libraries

CF and Digital Libraries

• Helps handle more electronic information

• Improve search results• Shapes direction of digital libraries• Supports collaboration on many levels

Nothing ventured, nothing gained.

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FundingFunding

• OSU Libraries Gray Chair for Innovative Technologies

• National Partnership for Advanced Computing Infrastructure (NSF)

• Georgia Pacific HMSC internship

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More informationMore information– Silence of the Sleeper

•Malcom Gladwell, The New Yorker, October 4th, 1999 (gladwell.com)

– System for Electronic Recommendation Filtering Prototype (SERF) for OSU Libraries• http://dl.nacse.org/osu

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ContactsContacts

Janet Webster– Oregon State University Libraries,

Hatfield Marine Science Center– janet.webster@oregonstate.edu

Jon Herlocker– Oregon State University, School

of Electrical Engineering & Computer Science

– herlock@eecs.oregonstate.edu