Empowering users to access information in the Digital Library Corin Anderson University of Washington

2 Empowering users DLs provide information to users Tricky: Not all users will be programmers –Non-programmer Web surfer –5 th grade student –Your grandmother How to cater to non-programming masses?

3 The Digital Library Many specialized DLs exist today –Medicine, literature, etc. Eventually, all DLs will be integrated: The DL Until then, use the Web as approximation

4 DL research at the UW Improving web search using popularity Automatic question answering Extracting information by demonstration Adaptive web sites

5 Popularity-based web search Want authoritative pages vs. Approximate authority by freq. of web visits –Data gathered from NCSA web proxies Rank query results based on popularity 1,000 hits 3 hits

6 Automatic question answering Return answers, not web pages, to queries “What’s the tallest mountain in the world?” vs. “Mount Everest” Search web for pages that contain answers –“the tallest mountain is” Heuristics for yes/no, “which is” questions

7 Information extraction Info from DL usually used elsewhere –Query stock history to build a graph in Excel – a list of current movies to a friend Extracting info is tricky –Special file formats (XML,.csv) arcane –Custom built wrappers to select tuples –Building wrappers isn’t easy, either! Solution: demonstrate a wrapper

8 ICE-9 Wrapper generation User demonstrates extracting info ICE-9 learns generalized program Demonstrate on very few instances

9 ICE-9 in action User demonstrated two instances ICE-9 steps through program correctly

10 ICE-9 – current work Collaborative demonstration ICE-9 predicts each step, asks for confirmation –If can’t predict with confidence, just ask user Active learning –ICE-9 suggests which example the user should demonstrate

11 Adaptive web sites Different users have different goals –But traditional web sites treat everyone the same –Everyone sees the same start page, query page, etc. Personalized sites can be customized –Customization is manual, tedious Want a site to learn users’ interest –Based on observed behavior, similarity to others –Adapt to individuals accordingly

12 Adaptations – structural Add link, remove link Add page (index page synthesis)

13 Adaptations – presentational Highlight link, content

14 From users to adaptations Users are clustered to find related visitors Models are fit to clusters to predict behavior Adaptation space is searched for best changes

15 AWS – current work Building, clustering user models Hierarchical user clustering –Users are leaf nodes, related groups interior –Influence of parent nodes decreases with distance Selecting adaptations from models –Choosing structural changes –Defining, selecting presentational changes

16 Summary Successful DLs cater to their users UW research concentrating on connecting users with information Look for us at IUI, KDD, ICML, AAAI, IJCAI, and elsewhere

17 ICE-9 in action ICE-9 learns from subsequent instances –Probabilities now 100%

18 ICE-9 – Version space algebra

19 Selecting adaptations Cluster models analyzed to determine interests “The user has an interest in the page” “The user visits the page by starting at the page – add a link between the two.”

20 User’s computerDate and time of visitRequested pageReferring page some-pc.cs22/Feb/ :49:13/- some-pc.cs22/Feb/ :49:23/edu/ some-pc.cs22/Feb/ :49:34/edu/courses/ some-other-pc.cs22/Feb/ :49:55/- some-pc.cs22/Feb/ :50:08/574http:// some-other-pc.cs22/Feb/ :50:20/info/current/