1. Adaptive Web Navigation for Wireless Devices Corin Anderson Pedro Domingos Dan Weld

2. 2 Web personalization Web sites designed “one size fits all” But one size does not fit all –Visitors will use the site in unforeseen ways –Browser may be resource-constrained –Unintuitive or impossible to use Personalization adapts and customizes content for each visitor In most need: mobile web visitors

3. 3 c|net on the desktop 1024x768 screen –Multi-column okay –Many links easily visible Fast network –Hierarchical link structure okay –Large images, HTML pages

4. 4 c|net on a wireless Palm Very small screen –Only few lines of text –Lots of scrolling Slow net connectivity –Following links costly Challenge: automatically improve PC-centric web site for mobile browsing

5. 5 Web site personalizers An intermediary between server and visitor Adapts and customizes site for each visitor Personalizations include: –Add shortcut links shorten long paths –Rearrange content to increase salience –Elide content, replacing with link VisitorPersonalizerWeb server

6. 6 Old and new 590AI – Autumn 2000 –Proteus –Shortcut links and content elision –Key ideas: Expected utility and search 590AI – Spring 2001 –MinPath –Shortcut links –Key ideas: clustering and predictive models

7. 7 Trails A trail is a sequence of page requests… …coherent in time… …and coherent in space

8. 8 Shortcuts Connect two previously unconnected pages Savings of shortcut is # links skipped –Don’t forget the link you follow – the shortcut itself!

9. 9 Shortcut link selection problem Given: –visitor V –trail prefix –maximum number of shortcuts m Output: –list of shortcuts that minimize the number of links the visitor must follow to reach the visitor’s destination

10. 10 Finding shortcuts If we know the whole trail finding the right shortcut is easy Unfortunately, omniscience is hard to come by

11. 11 Expectation All we really know is the prefix We must guess the rest of the trail Idea: –Foreach suffix of trail on site Calculate the probability of that suffix Add that probability to the shortcut to the end of that suffix –Return top m shortcuts

12. 12 Predictive models We don’t really guess the suffix – we try all of them on the site We calculate each probability using a model of behavior [[ predict the next request given the past requests, the position in the trail, and the visitor’s identity ]] We’ve tried a number of variations…

13. 13 Unconditional model Ignore visitor identity, trail prefix, position [[equation 1 from paper]] Of course, the visitor is bound to the links actually on the page; MinPath thus uses: [[next equation from paper]]

14. 14 Naïve Bayes mixture Unconditional builds one model for everyone Intuitively, we suspect that not everyone behaves the same Cluster visitors, and condition prediction on cluster identity

15. 15 Clustering Use Expectation-Maximization –Simultaneously cluster, build models Cluster sequences, not visitors …

16. 16 Clusters of unconditional models

17. 17 Markov models Condition on past history First order: one bit of history [[ equation ]] Also build mixtures of Markov models

18. 18 Request position Training data suggest ordinal position important We’ll try adding position to unconditional and Markov models

19. 19 Experiments Train models on 20 days of logs (35,000 trails) Test on 1.5 days (2,500 trails) All trails have length >= 3 (3 pages, 2 links) Measure performance as # links followed to reach destination

20. 20 Results Compare different models Compare different cluster sizes Compare cluster assignment method  MinPath does save navigational effort  Markov models offer most savings  Clustering helps

21. 21 figure 1 from ijcai paper

22. 22 Figure 2 from ijcai paper

23. 23 Clusters at www.cs www.cs Anecdotal clusters from www.cs datawww.cs

24. 24 Summary Shortcuts for navigation cluster model save 40%

25. 25 Ongoing work Proteus, MinPath feed on HTML and web graph But many sites are built from databases and templates What adaptations are possible given a declarative model of the site?