Models and interaction mechanisms for exploratory interfaces


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Models and interaction mechanisms for exploratory interfaces

  1. 1. COMO CAMPUS Models and interaction mechanisms for exploratory interfaces Luigi Spagnolo luigi.spagnolo@polimi.it1 Information and Communication Quality
  2. 2. Index2 ¨  PREVIEW: Online experimentation! ¨  Part I: navigation, search and exploration ¤  Break ¨  Part II: Faceted search: the model(s) and the interaction ¨  Visualization issues will be covered into an other lecture
  3. 3. 3 PREVIEW: Online Experimentation
  4. 4. Intro4 ¨  This lecture starts in a quite unusual way :-) ¨  To let you introduced with exploratory interfaces you’ll take part to a research experiment ¨  But don’t worry! ¤  It’s not dangerous for your health :-) ¤  The questionnaire you’re asked to fill is anonymous and the answers will not be graded
  5. 5. The application | 15
  6. 6. The application | 26 ¨  The last version of a prototype built for the Italian Ministry of Culture ¨  A map of exploring venues of archaeological interest in Italy ¨  According to three properties (facets): ¤  Kind of venue: museum, archaeological site and superintendence (a local branch of the Ministry of Culture devoted to archeological heritage management). ¤  Location: the venue location, at level of macro-area (Northern Italy, Central Italy, eyc.), Italian region and Italian province. ¤  Civilization or Period: The ancient civilizations (Romans, Greeks, etc.) or periods (e.g. Middle Ages, Bronze age) the venues are relevant to.
  7. 7. The application | 37 ¨  The tag cloud: ¤  Tag size à the number of results that are relevant with respect to the period or civilization in question. ¤  Text color à how much the percentage of results that are relevant for the period/ civilization deviates from an uniform distribution. n  Shades of green show a stronger positive correlation between the other selected filters (e.g. the location and/or the venue type) and the civilization/period in question. Red instead shows a negative correlation (the civilization/period is less significant with respect to other criteria selected). ¤  Background color à w.r.t. the whole set of venues are relevant for the period/ civilization, which percentage of them are included in the results? n  Green shows a positive correlation, while red instead shows a negative correlation. ¤  E.g., for venues in a specific region only (e.g. Lombardy), a green tagindicates that the given civilization was particularly relevant for that region. ¤  The green background shows instead that the civilization is peculiar of that region, and is less likely to be found elsewhere.
  8. 8. The application | 48 ¨  The map: ¤  At three levels: Italian region, Italian province, extact location(s) ¤  The color of the circle à the specific type of venue ¤  The size of the circle à the number of items of that type in that area
  9. 9. The experiment9 ¨  Go to ¤  (or ¨  You will find a page with two links: 1.  The application 2.  An online questionnarie (on Rational Survey) ¤  Keep both open on the browser ¨  Work individually (1 hour max) ¨  Answer with your opinions, without looking at other websites, just at the ArchaeoItaly application ¤  Remember: the survey is anonymous, and there are no “correct answers”! ¤  For any doubts, ask me!
  10. 10. 10 Part 1 | Navigation, search and exploration
  11. 11. Let’s start with a scenario11 ¨  Work in pairs ¨  Imagine to work as journalists for the Horse Illustrated magazine ¨  You have to write an essay about horses in art (and in particular in painting) among the centuries. ¨  Find interesting information on the website of the Louvre Museum ¤ home.jsp?bmLocale=en
  12. 12. Problems with the Louvre12 ¨  Artworks are separated by department (internal “bureaucratic” classification) and by provenience. ¨  It is not possible to search them together (regardless of their age and country of origin) by subject. ¨  There is no introductory content on the subject that can guide the student in her search.
  13. 13. Content-intensive websites13 ¨  Also know as: ¤  Information-intensive ¤  Often Infosuasive = informative + persuasive ¤  Like ancient rhetoric: inform and persuade ¨  Mainly intended for: ¤  Learning, understanding, discovering, comparing information ¤  Leisure and entertainment
  14. 14. Contents14 ¨  Text, multimedia (audio, video, images) ¨  Hypermedia = multimedia + hyperlinks ¨  Information involves subjective judgment ¤  Depends on the author and on the user ¤  Objective: “10km far from Como”, “the painting was made in 1886” ¤  Subjective: “Near Como”, “the painting is impressionist”
  15. 15. User experiences requirements | 115 ¤  From the users’ point of view: n  Usability: usage is effective, efficient and satisfactory n  Findability: users can locate what they are looking for n  “At a glance” understandabity: users understand the website coverage and can make sense of information n  Enticing explorability: users are compelled to “stay and play” and discover interesting connections among topics
  16. 16. User experiences requirements | 216 ¤  From the stakeholders’ point of view: n  Planned serendipity: promoting most important contents so that users can stumble in them n  E.g. “Readers that purchased this book also bought…” n  Communication strengh and branding: the website conveys the intended “message” and “brand” of the institution behind it n  E.g. “we have the lowest prices”, “we are very authorithative”, etc.
  17. 17. Information architecture17 ¤  Purpose: conceptually organizing information ¤  Providing access to contents n  Index navigation (a) n  Guided navigation (b) ¤  Providing the possibility of moving from a content to related ones n  Contextual navigation (c): cross- reference links, semantic relationships
  18. 18. “Traditional” structure18 ¤  Taxonomy: hierarchy of categories and subcategories n  Sections and group of contents are the branches of the tree n  Contents are the leaves ¤  Cross-reference links between nodes
  19. 19. An example19 ¤  Artworks of the month Sitemap: ¤  Paintings Top 10 masterpieces Art n  n  By artist gallery n  By artistic movement website n  By subject ¤  Sculptures n  ... n  By material ¤  Photographs n  ...
  20. 20. Problems/120 ¨  What if I want to browse all artworks (regardless their type) by artist? ¤  Classifications are “nested” in a fixed order ¤  Designers should choose which classification should prevail (e.g. by type) ¨  What if I want to find “impressionists paintings portraing animals”? ¤  I cannot combine multiple “sibling”classifications (e.g. by style and by subject)
  21. 21. Problems/221 ¨  As long as the website is small a good taxonomy can satisfy user requirements ¨  For large websites ¤  (hundreds or thousand of pages) ¤  Indexed/guided navigation doesn’t scale ¤  Users can’t easily find what they want ¤  Users can’t make sense of all such information
  22. 22. Solutions?22 ¨  What do users do when navigation doesn’t work? ¤  They use search! ¤  Search arranges contents dynamically and automatically (in a way not predefined by designers) ¨  But keyword-based search is not optimal ¤  No hints for users that have no clear idea of what looking for ¤  Users must know how the information is described (e.e. the specific jargon used) ¤  Just for retrieval/focalized search ¨  We need a better paradigm: Exploratory search
  23. 23. Exploratory search23 ¨  The model “query à results” is (too much) simple ¨  Search is often like berry picking! (Bates 1989) ¤  Users explore a corpus of contents ¤  They refine the query (again and again) according to what they learn ¤  They pick information here and there, piece by piece
  24. 24. From search to exploration24 ¨  From finding to understanding (Marchionini) ¤  Acquire knowledge about a domain, its jargon, the properties of information items in it. ¤  Useful to (better) understand what to look for ¤  …but also to analyze a dataset
  25. 25. Goals of exploratory applications25 ¨  Object seeking ¤  Identify the best object(s) whose features match user requirements (e.g. purchasing a photocamera with concerns regarding price, resolution, etc.) ¨  Knowledge seeking ¤  Expand the knowledge about a given topic and related information (e.g. Leonardo Da Vinci and Italian Renaissance) ¨  Wisdom seeking ¤  Discover interesting relationships among features in a information space/dateset (e.g. analysis of sales in Esselunga chain stores, according to store location, type of article, price, etc.) ¨  These goals can possibly coexist in the same application
  26. 26. Retrieval vs. exploration models26 ¨  Retrieval model: query + results ¤  Query can can be either: n  Free form (e.g. keyword based search) n  Structured (parametric search, e.g. Scholar advanced search) n  Guided (select data from a predefined set of choices) ¨  Exploration model: ¤  Query + results + refinements/feedback ¤  Query supported by self-adaptive structures for: n  Further filter results to a subset of them n  Summarizing the features shared by results
  27. 27. 27 Part 2 | Faceted search: model(s) and interaction (Amazon’s Diamond search was one of the first e-commerce applications of faceted search)
  28. 28. Faceted search28 ¨  A exploratory search/navigation pattern based on progressive filtering of results ¨  The user selects a combination of metadata values belonging to several facets ¨  Each facet correspond to a particular dimension that describes the content objects made available for search, e.g. for an artwork: ¤  Subject: people portrayed, flowers and plants, abstract... ¤  Medium: painting, sculpture, photography... ¤  Technique: oil, watercolors, digital art... ¤  Style: impressionism, expressionism, abstractism... ¤  Location: Prado, Louvre, Guggenheim
  29. 29. Let’s see a pair of examples29 ¨  Two examples: ¤ bin/flamenco.cgi/famuseum/Flamenco ¤ ¨  Try the same search we’ve seen before: find horses in art ¨  More examples at: morville/collections/ 72157603789246885/
  30. 30. Non just a matter of finding…30 E.g. you can learn that horses in art are often found in paintings portraing soldiers or warriors and leaders
  31. 31. How the interaction works31¨  When the user chooses a filter, the application selects: ¤  The results: items that have been “tagged” with the filter and the other metadata previously chosen ¤  The remaining filters: metadata that combined with the previous choices can produce results¨  The users can continue narrowing results until they options are available
  32. 32. A (generalized) formal model | 1 ( terms )32¨  Taxonomy: a pair T , ¤  A set of concepts or T = {t1 ,t2 ,…,tn } ¤  The subsumption relation connecting narrower terms (hyponyms) to broader concepts (hypernyms) laptop  computer location : Como  location : Lombardy  location : Italy ¤  Terminal concepts: terms not further specialized (the “leaves”)
  33. 33. A (generalized) formal model | 233¨  For faceted taxonomies concepts are given in terms of property-value pairs (restrictions): ¤  E.g. subject: “horse”, location: “Como”¨  A query is any of: q1 and q2 ¤  A restriction q = property : value q1 or q2 ¤  A conjunction, disjunction or negation of (sub)queries not q ¤  Actually there are limitations in the way concepts can be combined in current facet browser implementations
  34. 34. A (generalized) formal model | 334¨  Item description: an information item o ∈O is described as a conjunction of restrictions d ( o ) = subject :"horse" and style :"Impressionism" and …¨  Extension of a query: the set of items in a context O that match the query ext ( q ) = {o ∈O | d ( o)  q} O ext ( q1 and q2 ) ⊆ ext ( q1 ) , ext ( q2 ) ( ) tc  t p ⇒ ext ( tc ) ⊆ ext t p ext ( q1 ) , ext ( q2 ) ⊆ ext ( q1 or q2 ) ext ( not q ) ≡ ext ( ALL)  ext ( q )
  35. 35. A (generalized) formal model | 435¨  The result of a query is: ¤  Itsextension in the given information space extO ( q ) ¤  The set of features shared by these results: i.e. all the concepts that can be derived from the descriptions of objects in extO ( q )
  36. 36. Query transformations36¨  Operations allowing to navigate from a state to another of the exploratio ¤  Appending new restrictions to the query in conjunction (zoom-in: from a wider to a narrower set of results) ¤  Adding alternatives in disjunction to the existent ones (zoom- out: from a narrower to a wider set) ¤  Removing existing constraints (zoom-out again) ¤  Negating/excluding values ¤  Replacing a filter with another (shift)¨  Implemented by hyperlinks (for conjunctive filters / shift), check boxes (for disjunctions), etc.
  37. 37. How values are (usually) combined37 ¨  Filters belonging to different facets are combined in conjunction ¤  E.g. “technique:oil” AND “style:impressionism” ¤  Filters belonging to the same facet are: ¤  Combined in conjunction if the facet admits more values at the same time for each object n  E.g. “subject:people” AND “subject:animals” n  (both people and animals in the same picture) ¤  Combined in disjunction if the facet adimits only one value n  E.g. “location:Milan” OR “location:Como” n  (an object which is Como or in Milan)
  38. 38. Type of facets38 ¨  Single-valued (functional properties) vs. multi-valued ¨  Flat vs. hierarchical organization of values ¤  E.g. hierarchical: nation/region/province ¨  Subjective/arbitrary (properly named facets) vs. objective (attributes) ¤  A date, a location, a price are examples of objective data ¤  “Topic”, “Audience”, “Artistic movement”, “importance” are examples of subjective information ¤  Assigning/using a value involves some kind of judgment and interpretation and is influenced by cultural and personal backgrounds
  39. 39. Type of facet values39 ¨  Terms (strings of text) ¨  Sortable and comparable? ¤  Taxonomies, controlled ¤  We can say that vocabularies value1<=value2<=…<=valueN? ¤  User-defined tags ¤  E.g. Dates, magnitudes, scales of (folksonomies) judgment, quantitative data n  e.g. “sufficient”<“excellent”, ¤  From data-mining 10€<100€, “Monday”<“Friday” ¨  Numerical values and dates ¤  Ranges [value1, value2] ¨  Boolean values (yes/no) n  E.g. User is allowed to search for events from 01/06 to 31/08 ¤  E.g. “Available for buying?”, “original?”, “still living?” ¤  Classes of values n  e.g. for price: 0-10€, 11-20€, ¨  Even shades of color, 21-50€, 51-100€, … shapes, etc... n  The way we define classes is arbitrary and depend on domain
  40. 40. Benefits of faceted search40 ¨  Easy and natural almost like “traditional” browsing ¨  With respect to keyword-based search users have hints ¤  Users can more easily make sense of information (if supported by good interfaces) ¤  …and learn about the context by interacting with it ¨  Users can freely combine multiple classifications according to their wishes ¤  In traditional browsing, when you reach a terminal concept you can’t refine further ¤  With faceted search, you can continue refining with related concepts ¨  Navigation is safe: frustrating “no results found” searches avoided ¤  Only concepts that have been used to classify the current set of results are diplayed
  41. 41. Limitations41 ¨  It works well only with structured data ¨  Faceted search does not provide a ranking of results ¤  For “object seeking” tasks it might be a limitation ¤  It may be better to compute the “distance” with respect to an “optimal” solution à otimization task ¨  Other limitations are discussed in the following slides on advanced issues
  42. 42. 42 Advanced (research) issues
  43. 43. Full Boolean queries | 143 ¨  How to achieve something like this? “Given a budget of 250,000 euros, I’m interested in a flat with at least 4 rooms and not central heating in the centre, or an house with at least 5 rooms in the suburbs”
  44. 44. Full Boolean queries | 244 ¨  Foci (Ferré et al.) the set of sub-expressions in the semantic tree of the query ¨  ( ) A query is a pair q,φ , where q is an arbitrary combination of filters and φ is one of its foci ¤  The focus is used to select the subquery at which the new filter should be appended (or the transformation should be applied) ¤  …But also to “inspect” different points of view of information ¤  The main focus represents the “whole” query
  45. 45. Semantic faceted search45 ¨  We can filter items, but how can we filter facet values? ¤  E.g. paintings filtered by artists ¤  But how we filter the Artists facet values by nationality, gender, age, etc.? ¨  Exploring contents at level of sets using semantic relationships, e.g. ¤  The museums that have bronze Greek statues ¤  “Women portrayed by women”: paintings with subject:woman and artist:gender:female ¤  Schools attended by the daughters of U.S. democratic presidents ( ¤  Challenges: effective models and usable interface ¨  An example: Sewelis
  46. 46. Beyond binary classication | 1¤  Classification (faceted or not) is usually binary: ¤ An item must be either relevant (1) or not relevant (0) to a certain category ¤ Problem: quite arbitrary decision in many real domains
  47. 47. Beyond binary classication | 2î  How to classify acathedral by architectural style? ¤  Built upon a 6th century buliding ¤  Mainly gothic ¤  17th century (baroque) towers ¤  Rebuilt during neoclassicism ¤  Decorations added in 19th century ¤  Contains Roman forum marbles (donated by Pius IX) ¤  …î  Do we tag the cathedral with all or only some of these?î  A classification may be correct for a kind of users but ineffective for another one
  48. 48. Beyond binary classication | 3î Monna Lisa is a well known portait of a woman, but…î There is also a landscape in the backgroundî Do we classifity it as “subject: woman” and “subject: Tuscan landscape” too?
  49. 49. Beyond binary classication | 4î Onion is very used in French cuisineî How do we distinguish “onion-based” recipes from all the recipes with onion inside?
  50. 50. Beyond binary classication | 5¨  A possible solution: associating weights to each triple item- facet-value ¤  A statement about the statement¨  Values between 0 and 1 or other scales  ¨  Query could be specified in terms of facet-values pairs and ranges of weights
  51. 51. Beyond binary classication | 6¨  Subjective weights ¤  Relevance: at which extent the item can be considered as belonging to a certain facet value ¤  Significance: the relative importance of the item according to a facet value¨  Objective weights ¤  E.g. Concentration or quantity (e.g. a thing is made for the 10% of material:bronze) ¤  E.g. for exploring venues: distance from points of interests
  52. 52. Beyond binary classication | 7¨  Interaction (concepts)
  53. 53. Handling information overload53 ¨  Too more facets and facets values may generate information overload too! ¤  Possible solution: Display only the most relevant facets (and facet values) for the user profile or the given context ¨  How to determine the most “interesting” facets in a given context? ¤  E.g. those with a less “uniform” distribution of values (more correlation) ¤  We will discuss this in a next lecture… :-)
  54. 54. Interested in MS Theses? Contact us! :-)54 ¨  Advisors: Prof. Di Blas, Prof. Paolini ¨  Both theoretical and development ¨  Fuzzy facets ¨  Semantic faceted search ¨  Advanced visualizations ¨  … ¨  Your own ideas! :-)
  55. 55. 55 Any final questions? Are you still alive/awake? Thank you for your attention!