1 cni 2005 fall briefingtechlens techlens: exploring the use of recommenders to support users of...

1CNI 2005 Fall Briefing TechLens

TechLens: Exploring the Use of Recommenders to Support

Users of Digital Libraries

Joseph A. Konstan, Nishikant Kapoor,

Sean M. McNee, John T. Butler

GroupLens Research Project and University Libraries

University of Minnesota


Introduction

Challenges and Opportunities large digital collections of uneven quality and scope

continuing trend towards out-of-library usage of library collections

extensive collections of metadata citations and other linkage data (published and personally collected)

venue data expectations of personal service

increased prevalence of personalization


Recommenders

Tools to help identify worthwhile stuff Filtering interfaces

E-mail filters, clipping servicesSchedulable current awareness searches

Recommendation interfacesSuggestion lists, “top-n,” offers and promotions

Prediction interfacesEvaluate candidates, predicted ratings


Amazon.com


Wine.com Seeking


Cdnow album advisor


CDNow Album advisor recommendations


Classic CF

C.F. Engine

Ratings Correlations


Submit Ratings

C.F. Engine


ratings


Store Ratings

C.F. Engine


ratings


Compute

C.F. Engine


pairwise corr.


Request Recommendations

C.F. Engine


request


Identify Neighbors

C.F. Engine


find good …

Neighborhood


Select Items; Predict Ratings

C.F. Engine

Ratings CorrelationsNeighborhood

predictionsrecommendations


Understanding the Computation

Hoop Dreams

Star Wars

Pretty Woman

Titanic Blimp Rocky XV

Joe D A B D ? ? John A F D F Susan A A A A A A Pat D A C Jean A C A C A Ben F A F Nathan D A A


Hoop Dreams

Star Wars

Pretty Woman





First Steps …

• Established that citation web data can be used to effectively rate/recommend research papers

• Developed and evaluated a demonstration recommender to recommend additional citations for an existing paper (using its references) original demo used CiteSeer this version uses ACM digital library


DL Recs

C.F. Engine



DL Recs

C.F. Engine


Votes


DL Recs

C.F. Engine


Votes

Request


DL Recs

C.F. Engine


Votes

Request

Recommendations


Demonstration #1

Steps Select user Select paper Select algorithm See recommendations


What We Found

Results published in McNee et al. (CSCW 2002): Yes, we can make recommendations this way!

offline analysis showed that best algorithms could find half of recommendable withheld references in top 10, ¾ in top 40 recs

online experiments showed best algorithms gave recommendations more than half of which were relevant, and more than half of which were novel

Users like it! more than half of users felt useful (1/4 to 1/3 said not)

1-2 good recs out of 5 seemed sufficient for use Different algorithms have different uses

Further exploration in Torres et al. (JCDL 2004)


Phase II

Shifted our focus to ACM Digital Library

Greater exploration of user tasks: awareness services keeping track of a community

More automation find own bibliography from citations find collaborators

Thinking about “researcher’s desktop”


Demonstration #2

Steps: identify self see automated collections of citations and collaborators

show how to use collections for recommendation


Moving Forward

Collaboration Computer Scientists (HCI, recommenders) Librarians (field work, domain expertise, “real-life” service deployment)

Research methods Offline data gathering and feasibility studies

Online pilots and controlled experiments Online field studies (including random-assignment studies)


What’s Next?

Short-Term Efforts Task-specific recommendation Understanding personal bibliographies

Privacy issues

Longer-Term Efforts Toolkits to support librarians and other power users

Exploring the shape of disciplines Rights issues


Task-Specific Recommendations

Many different user needs awareness in area of expertise find specific work in area of expertise

explore peripheral or new area find people with relevant expertise

reviewers, program committees, collaborators

reading list for students, newcomersindividuals or groups

Different algorithms fulfill different needs


User Model Add any from:

CitationsAuthorsKeywordsTaxonomyAbstracts Full TextVenue

Recommendation Generator

ResultsChoose any:

CitationsAuthorsKeywordsTaxonomyVenue

Generic DL Recommendation Model

Data Repository

Info Need & Context

Rec. Algorithm

Filters

Rec. TuningEnd-UserPower-User

Tuning feedback


Personal Bibliographies

Working with RefWorks to explore bibliographies maintained by library users: how resolvable is personally-managed bibliographic data?

where does data come from (import/type) and is there sufficient quality control?

depth and span of bibliographies suitability for recommenders


Privacy Issues

Anything involving personal bibliographies, library usage is extremely sensitive what can we do with minimal personal data (e.g., explicit queries)? can we identify particularly sensitive cases?

can we de-personalize data for collaborative applications?

for what benefits will users give informed consent to use private data?

feasibility/efficacy of ratings in library domain


The Toolkit

What would it take to support complex requests? Help me assemble a collection of the 20 papers in molecular biology that have been most influential in other sciences

Help me assemble a committee of leading humanists who together span a collection of fields and have collaborated with most of the leaders of those fields

A new dimension of service for expert librarian


Describe a Discipline

Can we build automated tools to: identify the most important conferences and journals for a field?

identify the most important papers?seminal work from other fieldsseminal work that established this fieldnew work of particular influence

identify trends in topic? identify hubs of activity?


Rights Issues

Not our core expertise, but … rights issues are critical, particularlyuse of metadata, including abstractspossible future use of reviews

also important to understand and educate authors on future uses of their workeverything from rating systems to plagiarism detection


Discussion

Issues of your choice, or: privacy issues – are these a show-stopper?

will these tools change the nature of scholarship? is it already changing?can I cite each member of the program committee?

what will it take to demonstrate the value of such tools?

pragmatic issues of interoperability


Our Thanks

• GroupLens Research Group• U of M Libraries• NEC Research, ACM, RefWorks• NSF Grants: DGE 95-54517, IIS 96-13960, IIS 97-34442, IIS 99-78717, and IIS 01-02229 (and we hope more to come!)

• All the colleagues who’ve given us feedback along the way

• Our research subjects/users


TechLens: Exploring the Use of Recommenders to Support

Users of Digital Libraries

Joseph A. Konstan, Nishikant Kapoor,

Sean M. McNee, John T. Butler

GroupLens Research Project and University Libraries

University of Minnesota

1 cni 2005 fall briefingtechlens techlens: exploring the use of recommenders to support users of...

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