1. SIMS 213: User Interface Design & Development Marti Hearst Thurs Feb 15, 2001

2. Faceted Categories and Information Architecture In IS202 we learned about – Faceted category systems – Design of information architecture – But … not how to combine them This area is still developing This lecture reflects my thoughts on the topic. – Main points: need to integrate the information and navigation design with the search system This can be done through being smart about using faceted metadata

3. Web Search is Working! Survey finds high user satisfaction Study by npd group

4. Web Search is Working! Survey finds high user satisfaction (a recent upswing – the decline was caused by an increase in # of pages indexed)

5. Web Search is Working! Why? Queries are still short! Average query length currently ~2.4 words (Doug Cook, Inktomi)

6. My guess: Web Search is Successful at Finding Good Starting Points (home pages)

7. Evidence Web search engines are heavily using – Link analysis – Page popularity – Interwoven categories These all find dominant home pages

10. Consequences Web search engines are providing source selection! – A side note: A digital library issue as well. DL’s make people do this step explicitly. People don’t generally like this! What happens at the site? – Follow hyperlinks or use site search

11. Following Hyperlinks Works great when it is clear where to go next Frustrating when the desired directions are undiscernable or unavailable

12. Site Search This is not getting good reviews Large, disorganized results sets

13. An Analogy text search hypertext

14. Analogy Hypertext: – A fixed number of choices of where to go next; – A glance at the map tells you where you are; – But may not go where you want to go. To get from Topeka to Santa Fe, may have to go through Frostbite Falls Site Search: – Can go anywhere; – But may get stuck, disoriented, in a crevass!

15. Goal: An All-Tertrain Vehicle The best of both techniques – A vehicle that magically lays down track to suggest choices of where you want to go next based on what you’ve done so far and what you are trying to do – The tracks follow the lay of the land and go everywhere, but cross over the crevasses – The tracks allow you to back up easily

16. How to make an all-tertrain vehicle? Two ideas: Focus on the task. Use metadata explicitly.

17. The Importance of the Task Results from HCI suggest the importance of taking the task into account. Searching patent databases vs. Proving non-infringement Browsing newsgroups vs. Finding the denial-of-service hacker Getting all satellite news vs. Anticipating the competition

18. The Importance of the Task: Indirect Evidence How does Web page download time effect usability? – In one study, Spool found: (56kbit modem) Amazon: 36 sec/page (avg) About.com: 8 sec/page (avg) – Users rated the sites: Fastest: Amazon Slowest: About.com Why?

19. The Importance of the Task Perceived speed – Strong correlation between perceived speed and whether the users felt they completed their task – Strong correlation between perceived speed and whether the users felt they always knew what to do next (scent).

20. Metadata types Time/DateTopicRoleGeoRegion 

21. Content-based Metadata Medical text – Anatomy, Disease, Chemicals, Procedures… Architectural images – Location, Style, Materials, Period … Recipes! – Cuisine, Ingredients, Season, Calories … Example: – SOAR vs. epicurious

30. Epicurious Metadata Usage Advantages – Creates combinations of metadata on the fly – Different metadata choices show the same information in different ways – Previews show how many recipes will result – Easy to back up – Supports several task types ``Help me find a summer pasta,'' (ingredient type with event type), ``How can I use an avocado in a salad?'' (ingredient type with dish type), ``How can I bake sea-bass'' (preparation type and ingredient type)

31. Epicurious Metadata Usage Problem: lacks integration with search

32. What about Yahoo? Routes through the metadata are – Predefined – Unstable (due to symbolic links) – Long (due to bad mixing of metadata) – Example: Where is Berkeley? College and University > Colleges and Universities >United States > U > University of California > Campuses > Berkeley U.S. States > California > Cities >Berkeley > Education > College and University > Public > UC Berkeley

33. Yahoo using metadata well Yahoo restaurant guide combines: – Region – Topic (restaurants) – Related Information Other attributes (cuisines) Other topics related in place and time (movies)

34. Green: restaurants & attributes Red: related in place & time Yellow: geographic region

35. Combining Information Types Region – State City A & E – Film – Theatre – Music – Restaurants California Eclectic Indian French Assumed task: looking for evening entertainment

36. Other Possible Combinations Region + A&E City + Restaurant + Movies City + Weather City + Education: Schools Restaurants + Schools …

37. Bookstore preview combinations topic + related topics topic + publications by same author topic + books of same type but related topic

38. Problems with Metadata Usage Standard approaches – Paths are hand-edited, predefined – Not well-integrated with search – Not tailored to task as it develops – Not personalized – Not dynamic

39. A new project: FLAMENCO FLexible Access using MEtadata in Novel COmbinations Main ideas: – Make metadata an explicit part of the interface, but in a highly-usable manner – Preview and postview choices – Determine views dynamically and (semi) automatically, using a task-based model

40. Flamenco: Dynamic Previews Medical example – Allow user to select metadata in any order – At each step, show different types of relevant metadata, based on prior steps and personal history, include # of documents – Previews restricted to only those metadata types that might be helpful

41. Dynamic Metadata Previews How different from Yahoo & Amazon? – Dynamically determine what to show next Yahoo’s combos are predefined Amazon’s are also predefined, and limited to taste and general topic only A way to seamlessly integrate – Related topics – User preferences (personalization) – Context-sensitivity

42. Application to Image Search

43. Image Search Content analysis is making strides Rich hand-assigned metadata is available But most search based on – Keyword matching (alltheweb/lycos multimedia) – Image-component based querying (QBIC) – Overall similarity to sample image (Blobworld) – Combo of keyword and image component

44. Image Search: What is the task? Illustrate my slides? – “Find a crevasse” – Keyword match works pretty well Find inspiration for an architectural design? – General similarity: maybe There are many research projects exploring how to use image recognition technology in search Blobworld at UCB (Forsyth, Malik, et al) – But more control might be better How to incorporate metadata?

45. Blobworld

46. Architects’ Image Use Work practices – Observations from personal design experience, and surveys of designers – Common activities for image use Browsing most common at early stages of design Collage making, sketching, pinning up on walls Cultural and social practice – Designers learn how to do this in schools – Ways of communicating with images varies with organization

47. Current Problems Browsing Images On-Line Current on-line image collections offer few advantages over paper collections Lose papers’ ease of manipulation Little gain in accessibility Queries are textual and must be well-formed – Not appropriate for the early phases of design when image browsing is critical Image search engines don’t follow good UI design in general – Poor support for search starting points, collection visualization

48. How different from recipe example? More open-ended Easier to scan many images quickly Tertrain metaphor not used here – Not narrowing down a large set – Rather, always viewing more images – A mechanism for “steering” through the metadata

49. SPIRO: >40,000 art & architecture images Detailed metadata

50. SPIRO Query Form

51. SPIRO query on Subject: church

52. A Better Approach Greatbuildings.com Good use of hyperlinks on metadata – But search isn’t all that helpful Caveat: a small collection – ~1000 buildings – ~4500 images total

53. GreatBuildings Metadata Search Form

54. GreatBuildings Starting Point

56. GreatBuildings Use of Hyperlinked Metadata

59. GreatBuildings: Image Browsing Interface

60. Our Approach Create a software architecture that allows experimentation with different approaches Add functionality in a stepwise fashion Architecture task: – Emphasize images over text – Use greatbuildings-style interface as a reasonable baseline for comparison – Find out how much choice is too much – Find out whether explicit metadata is better than implicit more- like-this

61. Evaluation Methodology Regression Test – Select a set of tasks Use these throughout the evaluation – Start with a baseline system Evaluate using the test tasks – Add a feature Evaluation again Compare to baseline Only retain those changes that improve results

62. Summary Standard search is too flexible Hyperlinks too restrictive Flamenco Goals: Task-centric search interfaces Integrate metadata with search Dynamic previews Easily retrace steps Systematically determine what works for real users