Collaborative Filtering:
Possibilities for Digital Libraries
Jon Herlocker
Janet Webster
Seikyung Jung
Oregon State University
CNI 2003/Herlocker, Jung, and Webster
1
Current search engines
are insufficient.
CNI 2003/Herlocker, Jung, and Webster
2
Two important search
engine problems
• They don’t understand:
– Quality
– Context
CNI 2003/Herlocker, Jung, and Webster
3
But First: Our Context
• Why are we standing up
here?
• We think we can improve
the digital library
experience.
CNI 2003/Herlocker, Jung, and Webster
4
Today’s Context
1.
2.
3.
4.
Research questions & hypotheses
Collaborative filtering
Our approach to CF in the Library
Challenges of collaborative filtering
for library search
5. Initial lessons learned
CNI 2003/Herlocker, Jung, and Webster
5
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?
CNI 2003/Herlocker, Jung, and Webster
6
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?
CNI 2003/Herlocker, Jung, and Webster
7
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
CNI 2003/Herlocker, Jung, and Webster
8
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
CNI 2003/Herlocker, Jung, and Webster
9
CF 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.
CNI 2003/Herlocker, Jung, and Webster
10
Our 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
CNI 2003/Herlocker, Jung, and Webster
11
CNI 2003/Herlocker, Jung, and Webster
12
CNI 2003/Herlocker, Jung, and Webster
13
CNI 2003/Herlocker, Jung, and Webster
14
The 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
CNI 2003/Herlocker, Jung, and Webster
15
CS 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?
CNI 2003/Herlocker, Jung, and Webster
16
Library 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?
CNI 2003/Herlocker, Jung, and Webster
17
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
CNI 2003/Herlocker, Jung, and Webster
18
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.
CNI 2003/Herlocker, Jung, and Webster
19
Funding
• OSU Libraries Gray Chair for
Innovative Technologies
• National Partnership for
Advanced Computing
Infrastructure (NSF)
• Georgia Pacific HMSC
internship
CNI 2003/Herlocker, Jung, and Webster
20
More 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
CNI 2003/Herlocker, Jung, and Webster
21
Contacts
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
CNI 2003/Herlocker, Jung, and Webster
22