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Collaborative Filtering: Possibilities for Digital Libraries

This presentation explores the possibilities of using collaborative filtering to improve the digital library experience. It discusses the challenges and benefits of implementing collaborative filtering in libraries and presents research hypotheses and initial lessons learned. The talk also addresses the questions and concerns of both librarians and computer scientists regarding access to information and the limitations of current search technology.

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Collaborative Filtering: Possibilities for Digital Libraries

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  1. Collaborative Filtering:Possibilities for Digital Libraries Jon Herlocker Janet Webster Seikyung Jung Oregon State University CNI 2003/Herlocker, Jung, and Webster

  2. Current search engines are insufficient. CNI 2003/Herlocker, Jung, and Webster

  3. Two important search engine problems • They don’t understand: • Quality • Context CNI 2003/Herlocker, Jung, and Webster

  4. 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

  5. Today’s Context • Research questions & hypotheses • Collaborative filtering • Our approach to CF in the Library • Challenges of collaborative filtering for library search • Initial lessons learned CNI 2003/Herlocker, Jung, and Webster

  6. 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

  7. 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

  8. 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

  9. 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

  10. 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

  11. 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

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  15. 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

  16. 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

  17. 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

  18. 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

  19. 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

  20. 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

  21. 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

  22. 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

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