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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
2CNI 2005 Fall Briefing TechLens
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
3CNI 2005 Fall Briefing TechLens
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
4CNI 2005 Fall Briefing TechLens
Amazon.com
5CNI 2005 Fall Briefing TechLens
Wine.com Seeking
6CNI 2005 Fall Briefing TechLens
Cdnow album advisor
7CNI 2005 Fall Briefing TechLens
CDNow Album advisor recommendations
8CNI 2005 Fall Briefing TechLens
Classic CF
C.F. Engine
Ratings Correlations
9CNI 2005 Fall Briefing TechLens
Submit Ratings
C.F. Engine
Ratings Correlations
ratings
10CNI 2005 Fall Briefing TechLens
Store Ratings
C.F. Engine
Ratings Correlations
ratings
11CNI 2005 Fall Briefing TechLens
Compute
C.F. Engine
Ratings Correlations
pairwise corr.
12CNI 2005 Fall Briefing TechLens
Request Recommendations
C.F. Engine
Ratings Correlations
request
13CNI 2005 Fall Briefing TechLens
Identify Neighbors
C.F. Engine
Ratings Correlations
find good …
Neighborhood
14CNI 2005 Fall Briefing TechLens
Select Items; Predict Ratings
C.F. Engine
Ratings CorrelationsNeighborhood
predictionsrecommendations
15CNI 2005 Fall Briefing TechLens
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
16CNI 2005 Fall Briefing TechLens
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
Understanding the Computation
17CNI 2005 Fall Briefing TechLens
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
Understanding the Computation
18CNI 2005 Fall Briefing TechLens
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
Understanding the Computation
19CNI 2005 Fall Briefing TechLens
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
Understanding the Computation
20CNI 2005 Fall Briefing TechLens
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
Understanding the Computation
21CNI 2005 Fall Briefing TechLens
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
Understanding the Computation
22CNI 2005 Fall Briefing TechLens
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
23CNI 2005 Fall Briefing TechLens
DL Recs
C.F. Engine
Ratings Correlations
24CNI 2005 Fall Briefing TechLens
DL Recs
C.F. Engine
Ratings Correlations
Votes
25CNI 2005 Fall Briefing TechLens
DL Recs
C.F. Engine
Ratings Correlations
Votes
26CNI 2005 Fall Briefing TechLens
DL Recs
C.F. Engine
Ratings Correlations
Votes
27CNI 2005 Fall Briefing TechLens
DL Recs
C.F. Engine
Ratings Correlations
Votes
Request
28CNI 2005 Fall Briefing TechLens
DL Recs
C.F. Engine
Ratings Correlations
Votes
Request
29CNI 2005 Fall Briefing TechLens
DL Recs
C.F. Engine
Ratings Correlations
Votes
Request
Recommendations
30CNI 2005 Fall Briefing TechLens
Demonstration #1
Steps Select user Select paper Select algorithm See recommendations
31CNI 2005 Fall Briefing TechLens
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)
32CNI 2005 Fall Briefing TechLens
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”
33CNI 2005 Fall Briefing TechLens
Demonstration #2
Steps: identify self see automated collections of citations and collaborators
show how to use collections for recommendation
34CNI 2005 Fall Briefing TechLens
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)
35CNI 2005 Fall Briefing TechLens
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
36CNI 2005 Fall Briefing TechLens
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
37CNI 2005 Fall Briefing TechLens
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
38CNI 2005 Fall Briefing TechLens
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
39CNI 2005 Fall Briefing TechLens
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
40CNI 2005 Fall Briefing TechLens
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
41CNI 2005 Fall Briefing TechLens
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?
42CNI 2005 Fall Briefing TechLens
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
43CNI 2005 Fall Briefing TechLens
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
44CNI 2005 Fall Briefing TechLens
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
45CNI 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