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Collaborative Filtering: Possibilities for Digital Libraries

Collaborative Filtering: Possibilities for Digital Libraries. Jon Herlocker Janet Webster Seikyung Jung Oregon State University. Current search engines are insufficient. Two important search engine problems. They don’t understand: Quality Context. But First: Our Context.

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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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