1 Flexible Search and Navigation using Faceted Metadata Prof. Marti Hearst University of California, Berkeley Search Engines Meeting, April 2002 Research funded by: NSF CAREER Grant, NSF IIS

2 The Flamenco Project Team Ame Elliott Jennifer English Marti Hearst Rashmi Sinha Kirsten Swearingen Ping Yee

3 Motivation Web search works well now –Gets people to the appropriate web sites –Finds starting points Web SITE search is NOT ok –Results still overwhelming –Not well-integrated with the information architecture –People prefer to follow links anyhow

4 Recent Study by Vividence Research Spring 2001, 69 web sites –70% eCommerce –31% Service –21% Content – 2% Community The most common problems: 53% had poorly organized search results 32% had poor information architecture 32% had slow performance 27% had cluttered home pages 25% had confusing labels 15% invasive registration 13% inconsistent navigation

5 Following Hyperlinks Works great when it is clear where to go next Frustrating when the desired directions are undetectable or unavailable Free Text Search Can specify anything Can result in a disorganized mess

6 An Analogy text search hypertext Wanted: An All TerTrain Vehicle!

7 Main Idea Integrate the search seamlessly into the information architecture Use hierarchical metadata to –Allow flexible navigation –Provide query previews –Organize search results –Both expand and refine the search

8 The Challenges Users don’t like new search interfaces How to show lots more information without overwhelming or confusing?

9 Main Idea Use metadata to show where to go next –More flexible than canned hyperlinks –Less complex than full search –Help users see and return to what happened previously

10 An Important Trend in Information Architecture Design Generating web pages from databases Implications: –Web sites can adapt to user actions –Web sites can be instrumented

11 A Taxonomy of WebSites low high Complexity of Applications Complexity of Data From: The (Short) Araneus Guide to Website development, by Mecca, et al, Proceedings of WebDB’99, Catalog Sites Web-based Information Systems Web- Presence Sites Service- Oriented Sites

12 Faceted Metadata

13 Metadata: data about data Facets: orthogonal categories Time/DateTopicGeoRegion 

14 Faceted Metadata: Biomedical MeSH (Medical Subject Headings)

15 Mesh Facets (one level expanded)

16 Questions we are trying to answer How many facets are allowable? Should facets be mixed and matched? How much is too much? Should hierarchies be progressively revealed, tabbed, some combination? How should free-text search be integrated?

17 How NOT to do it Yahoo uses faceted metadata poorly in both their search results and in their top-level directory They combine region + other hierarchical facets in awkward ways

18 Yahoo’s use of facets

19 Yahoo’s use of facets

20 Yahoo’s use of facets

21 Yahoo’s use of facets 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

22 Problem with Metadata Previews as Currently Used –Hand edited, predefined –Not tailored to task as it develops –Not personalized –Often not systematically integrated with search, or within the information architecture in general

23 Recipe Collection Examples

24 From soar.berkeley.edu (a poor example)

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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 + event type), “How can I use an avocado in a salad?'' (ingredient type + dish type), “How can I bake sea-bass'' (preparation type + ingredient type)

31 Metadata usage in Epicurious PrepareCuisineIngredientDish Recipe

32 Metadata usage in Epicurious PrepareCuisineIngredientDish PrepareCuisineDish I Select

33 Metadata usage in Epicurious PrepareCuisineIngredientDish I > Group by PrepareCuisineDish

34 Metadata usage in Epicurious PrepareCuisineIngredientDish PrepareCuisineDish I > Group by

35 Metadata usage in Epicurious PrepareCuisineIngredientDish PrepareCuisineDish I > Group by PrepareCuisine I Select

36 Recipe Information Architecture Information design –Recipes have five types of metadata categories Cuisine, Preparation, Ingredients, Dish, Occasion Each category has one level of subcategories

37 Recipe Information Architecture Navigation design –Home page: show top level of all categories –Other pages: A link on an attribute ANDS that attribute to the current query; results are shown according to a category that is not yet part of the query A change-view link does not change the query, but does change which category’s metadata organizes the results

38 Epicurious Basic Search Lacks integration with metadata

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40 Epicurious: Usability Study People liked the browsing-style metadata-based search and found it helpful People sometimes preferred the advanced search –For more constrained tasks –But zero results are frustrating People dissprefer the standard simple search

41 Missing From Epicurious How to scale? –Hierarchical facets –Larger collection How to integrate search? How to allow expansion in addition to refinement?

42 Metadata Interface for Image Search

43 Current Approaches to Image Search Visual Content and Cues, e.g., QBIC (Flickner et al. ‘95) Blobworld (Carson et al. ‘99) Body Plans (Forsyth & Fleck ‘00) –Color, texture, shape –Move through a similarity space Keyword based –Piction (Srihari ’91) –WebSeek (Smith and Jain ’97) –Google image search

44 Architects and City Planners Common activitie: –Use images for inspiration Browsing during early stages of design –Collage making, sketching, pinning up on walls This is different than illustrating powerpoint Maintain sketchbooks & shoeboxes of images –Young professionals have ~500, older ~5k No formal organization scheme –None of 10 architects interviewed about their image collections used indexes Do not like to use computers to find images

45 The Collection ~40,000 images from the UCB architecture slide library The current database and interface is called SPIRO Very rich, faceted, hierarchical metadata

46 Architects’ Image Use Common activitie: –Use images for inspiration Browsing during early stages of design –Collage making, sketching, pinning up on walls –This is different than illustrating powerpoint Maintain sketchbooks & shoeboxes of images –Young professionals have ~500, older ~5k No formal organization scheme –None of 10 architects interviewed about their image collections used indexes Do not like to use computers to find images

47 Development Timeline Needs assessment. –Interviewed architects and conducted contextual inquiries. Lo-fi prototyping. –Showed paper prototype to 3 professional architects. Design / Study Round 1. –Simple interactive version. Users liked metadata idea. Design / Study Round 2: –Developed 4 different detailed versions; evaluated with 11 architects; results somewhat positive but many problems identified. Matrix emerged as a good idea. Metadata revision. –Compressed and simplified the metadata hierarchies Design / Study Round 3. –New version based on results of Round 2 –Highly positive user response

48 The Interface Nine hierarchical facets –Matrix –SingleTree Chess metaphor –Opening –Middlegame –Endgame Tightly Integrated Search Expand as well as Refine Intermediate pages for large categories

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57 Usability Study on Round 3 19 participants –Architecture/City Planning background Two versions of the interface –Tree (one hierarchical facet at a time) –Matrix (multiple hierarchical facets) Several tasks Subjective responses –All highly positive –Very strong desire to use the interface in future –Will replace the current SPIRO interface

58 Study Tasks 1.High Constraint Search: Find images with metadata assigned from 3 facets (e.g., exterior views of temples in Lebanon) 1.1) Start by using a Keyword Search 1.2) Start by Browsing (clicking a hyperlink) 1.3) Start by using method of choice 2.Low Constraint Search: Find a low-constraint set of images (metadata in one facet) 3.Specific Image Search: Given a photograph and no other info, find the same image in the collection 4.Browse for Images of Interest

59 Interface Evaluation Users rated Matrix more highly for: –Usefulness for design work –Seeing relationships between images –Flexibility –Power On all except “find this image” task, users also rated the Matrix higher for: –Feeling “on track” during search –Feeling confident about having found all relevant images

60 Overall Preferences: Matrix vs. Tree Simple search (e.g. images of deserts) Complex search (e.g. exteriors of temples in Lebanon) Find images like this one OVERALL PREFERENCE Matrix Tree 5433

61 User Comments - Matrix “Easier to pursue other queries from each individual page” “Powerful at limiting and expanding result sets. Easy to shift between searches.” “Keep better track of where I am located as well as possible places to go from there.” “Left margin menu made it easy to view other possible search queries, helped in trouble-shooting research problems.” “Interface was friendlier, easier, more helpful.” “I understood the hierarchical relationships better.”

62 User Comments – Tree Pro –“Simple” –“More typical of other search engines I’d use” –“Visually simpler and more intuitive…Matrix a bit overwhelming with choices.” Con –“I found SingleTree difficult to use when I had to refine my search on a search topic which I was not familiar with. I found myself guessing.” –“SingleTree required more thought to use and to find specific images.” –“I do not trust my typng and spelling skills. I like having categories.”

63 Task Completion Times (Find Image is an artificial task: given a photo and no other info, find it in the collection.)

64 When Given A Choice … For each interface, one task allowed the user to start with either a keyword search or the hyperlinks. 3 chose to search in both interfaces 11 chose to browse in both interfaces 4 chose to search in Matrix, browse in Tree 1 chose to browse in Matrix, search in Tree

65 Feature Usage (%) Refining

66 Feature Usage – Expanding / Starting Over

67 Feature Usage (%) Types of Actions

68 Interface Evaluation Users rated Matrix more highly for: –Usefulness for design work –Seeing relationships between images –Flexibility –Power On all except “find this image” task, users also rated the Matrix higher for: –Feeling “on track” during search –Feeling confident about having found all relevant images

69 Application to Medline

70 Summary and Conclusions

71 Summary Two Usability Studies Completed –Recipes: 13,000 items –Architecture Images: 40,000 items Conclusions: –Users like and are successful with the dynamic faceted hierarchical metadata, especially for browsing tasks –Very positive results, in contrast with studies on earlier iterations –Note: it seems you have to care about the contents of the collection to like the interface

72 Summary Validating an approach to web site search –Use hierarchical faceted metadata dynamically, integrated with search Many difficult design decisions –Iterating and testing was key Bits and pieces were there in industry –The approach is being picked up too –One is very similar now: endeca.com

73 Summary We have addressed several interface problems: –How to seamlessly integrate metadata previews with search Show search results in metadata context “Disambiguate” search terms –How to show hierarchical metadata from several facets The “matrix” view Show one level of depth in the “matrix” view –How to handle large metadata categories Use intermediate pages –How to support expanding as well as refining Still working on it to some extent

74 Advantages of the Approach Supports different search types –Highly constrained known-item searches –Open-ended, browsing tasks –Can easily switch from one mode to the other midstream –Can both expand and refine Allows different people to add content without breaking things Can make use of standard technology

75 Some Unanswered Questions How to integrate with relevance feedback (more like this)? –Would like to use blobworld-like features How to incorporate user preferences and past behavior? How to combine facets to reflect tasks?

76 Thank you! bailando.sims.berkeley.edu/flamenco.html For more information: