1. Ed H. Chi IMA Digital Library Workshop 2001-02-23 1 Ed H. Chi www.geekbiker.com U of Minnesota Ph.D.: Visualization Spreadsheets M.S.: Computational Biology Expertise: InfoVis, Study of the Web, TaeKwonDo, Poetry, Motorcycling, Pottery

2. Information Scent Modeling User Browsing Strategies on the Web Ed H. Chi Peter Pirolli User Interface Research Group This research was supported in part by Office of Naval Research contract number 'N00014-96-C-007'.

3. Ed H. Chi IMA Digital Library Workshop 2001-02-23 3 Comparison to Library Experience tells us: general layout of content –which floor, which section. which books are of greatest interest –by the wear on the spines. which information is timely or deadwood –by looking at the circulation check-out stamps inside the book covers.

4. Ed H. Chi IMA Digital Library Workshop 2001-02-23 4 Trends and Problems 200M Web users, 6M web sites Web design process ad-hoc, not optimal Some tools extract behaviors and correlations but not intentionally Being successful requires making the Web more useful and usable to a broader audience

5. Ed H. Chi IMA Digital Library Workshop 2001-02-23 5 Information Foraging Amount of AccessibleKnowledge AccessibleKnowledge Cost [Time]

6. Ed H. Chi IMA Digital Library Workshop 2001-02-23 6 Underlying Concept Users seeking information is similar to hunter/gatherers optimization strategies.

7. Ed H. Chi IMA Digital Library Workshop 2001-02-23 7 Underlying Concept Information Scent is the user perception of the cost and value of information. –Similar to hunters following animal foot prints.

8. Ed H. Chi IMA Digital Library Workshop 2001-02-23 8

9. 9 Information Scent Users forage by surfing along links Foragers use proximal cues (text snippets or graphics) to access distal content (destination page) Scent is the proximal perception of value and cost of distal content contentlink snippet

10. Ed H. Chi IMA Digital Library Workshop 2001-02-23 10Assumptions Users have information goals, their surfing patterns are guided by information scent Two questions –Given an information goal and a starting point Where do users go? (Behavior) –Given some surfing pattern What is the user’s goal? (Need)

11. Ed H. Chi IMA Digital Library Workshop 2001-02-23 11 WUFIS: Web User Flow by Information Scent User Information goal Web site Web Page content links Web user flow simulation Predicted paths

12. Ed H. Chi IMA Digital Library Workshop 2001-02-23 12 How does it work? Start users at page with some goal Flow users through the network Examine user patterns

13. Ed H. Chi IMA Digital Library Workshop 2001-02-23 13 WUFIS Algorithm Weight MatrixQuery 1 Relevant Documents

14. Ed H. Chi IMA Digital Library Workshop 2001-02-23 14 WUFIS Algorithm (cont.) R = Relevant documents T = Topology matrix 2 Scent Matrix

15. Ed H. Chi IMA Digital Library Workshop 2001-02-23 15 Prelim. Evaluation of WUFIS Show that WUFIS generates good URL destinations based on information need. 19 Websites Size: 27-12,000 pages Info Provider, eCommerce, Large Corp. Info Need from very general (product info) to very specific (migraine headaches) Top ten URL position simulated are extracted. Each URL is blindly rated for relevancy.

16. Ed H. Chi IMA Digital Library Workshop 2001-02-23 16 WUFIS Evaluation 570 ratings are collected = 3 variations of the algorithm x 10 URLs x 19 sites Tabulated, Averaged. Result = 7.54 (out of 10) 19 Websites Website Info, Algorithm Performance

17. Ed H. Chi IMA Digital Library Workshop 2001-02-23 17 IUNIS: Inferring User Need by Info Scent User Information goal Web site Web Page content links Web user flow simulation observed paths

18. Ed H. Chi IMA Digital Library Workshop 2001-02-23 18 Extracting Paths Longest Repeating Sequence (LRS) New path mining technique Extracts significant surfing paths Reduces the complexity of path model

19. Ed H. Chi IMA Digital Library Workshop 2001-02-23 19IUNIS WeightPath P = observed user path T = topology matrix W = word x document weights K = relevant keywords 2 1 TopologyPath

20. Ed H. Chi IMA Digital Library Workshop 2001-02-23 20 Evaluation of IUNIS Goal: Show that keyword summaries produced by IUNIS are good at communicating the content of the user paths. Dataset: 8 participants random 10 paths from (5/18/1998, xerox.com, path length=6) booklets of pages on paths (in order)

21. Ed H. Chi IMA Digital Library Workshop 2001-02-23 21 Evaluation of IUNIS Procedure: Single rating sheet with the ten 20-word summaries. Beside each summary, users are asked to rate the summaries on a 5-point Likert Scale. A copy of this rating sheet is attached to each of the ten path booklets Users are asked to read through each booklet and rate each of the path summaries. User are also asked to identify which of the ten summaries was the best match.

22. Ed H. Chi IMA Digital Library Workshop 2001-02-23 22 Evaluation of IUNIS Results: Matching summary mean = 4.58 (median=5) Non-matching summary mean = 1.97 (median=1) Difference highly significant (p <.001) Best match summary: 5.6 out of 10 (Cohen Kappa=0.51) Evaluation yield strong evidence that IUNIS generates good summaries of the Web paths.

23. Ed H. Chi IMA Digital Library Workshop 2001-02-23 23 ScentViz Tasks Overall site High-level traffic flow and routes? Ease of access and costs? Given a specific Web page Where do users come from? Where do they go? What other pages are related? Users What are interests of the users? Where should they go based on their need? Do observed data match simulation?

24. Ed H. Chi IMA Digital Library Workshop 2001-02-23 24 Visualization Demo Dome Tree Usage Based Layout Path Embedding

25. Ed H. Chi IMA Digital Library Workshop 2001-02-23 25 Scenario 1: Page Types Multi-way branching point investor/sitemap.htm

26. Ed H. Chi IMA Digital Library Workshop 2001-02-23 26 Scenario 1: Drill-down Few well-traveled future paths shareholder info 1998 fact book financial doc order Conclusion good local sitemap

27. Ed H. Chi IMA Digital Library Workshop 2001-02-23 27 Scenario 2: Well-traveled Related information all over the site One well-worn path on the left relating to product tutorial Scansoft/tbpro98win/index.htm

28. Ed H. Chi IMA Digital Library Workshop 2001-02-23 28 Scenario 3: Identify Need Need of path from shareinfo to orderdoc reinvestment stock brochure dividend shareholder investor/sitemap.htm

29. Ed H. Chi IMA Digital Library Workshop 2001-02-23 29 Scenario 4: Scent Predict Scent computed based on “pagis” need Good match between scent and LRS paths Scansoft/pagis/index.html

30. Ed H. Chi IMA Digital Library Workshop 2001-02-23 30 InfoScent Summary The overall goal is to model Web user information needs Bridge gap between clicks and information needs Predict user navigation behavior Develop new applications and Web usability metrics

31. Ed H. Chi IMA Digital Library Workshop 2001-02-23 31Questions? Ed H. Chi Chi@acm.org http://www.geekbiker.com