Models and interaction mechanisms for exploratory interfaces

COMO CAMPUS

Models and interaction mechanisms for exploratory interfaces Luigi Spagnolo [email protected]

1 Information and Communication Quality

Index 2

¨  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 PREVIEW: Online Experimentation

Intro 4

¨  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

The application | 1 5


¨  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.


¨  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.


¨  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

The experiment 9

¨  Go to http://tinyurl.com/exp-icq ¤  (or http://www.ellesseweb.com/mining/)

¨  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 Part 1 | Navigation, search and exploration

Let’s start with a scenario 11

¨  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 ¤  http://www.louvre.fr/llv/commun/

home.jsp?bmLocale=en

Problems with the Louvre 12

¨  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.

Content-intensive websites 13

¨  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

Contents 14

¨  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”

User experiences requirements | 1 15

¤  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

User experiences requirements | 2 16

¤  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.

Information architecture 17

¤  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

“Traditional” structure 18

¤ 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

An example 19

Sitemap:

Art gallery website

¤  Artworks of the month ¤  Paintings

n  Top 10 masterpieces n  By artist n  By artistic movement n  By subject

¤  Sculptures n  ... n  By material

¤  Photographs n  ...

Problems/1 20

¨  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)

Problems/2 21

¨  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

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

Exploratory search 23

¨  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

From search to exploration 24

¨  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

Goals of exploratory applications 25

¨  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

Retrieval vs. exploration models 26

¨  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 Part 2 | Faceted search: model(s) and interaction

(Amazon’s Diamond search was one of the first e-commerce applications of faceted search)

Faceted search 28

¨  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

Let’s see a pair of examples 29

¨  Two examples: ¤  http://orange.sims.berkeley.edu/cgi-

bin/flamenco.cgi/famuseum/Flamenco ¤  http://www.artistrising.com

¨  Try the same search we’ve seen before: find horses in art

¨  More examples at: http://www.flickr.com/photos/morville/collections/72157603789246885/

Non just a matter of finding… E.g. you can learn that horses in art are often found in paintings portraing soldiers or warriors and leaders

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How the interaction works 31

¨  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

A (generalized) formal model | 1 32

¨  Taxonomy: a pair ¤ A set of concepts or terms ¤ The subsumption relation connecting narrower

terms (hyponyms) to broader concepts (hypernyms) ¤ Terminal concepts: terms not further specialized

(the “leaves”)

T ,( ) T = t1,t2 ,…,tn{ }

laptop computerlocation : 'Como ' location : 'Lombardy ' location : 'Italy '


¨  For faceted taxonomies concepts are given in terms of property-value pairs (restrictions): ¤  E.g. subject: “horse”, location: “Como”

¨  A query is any of: ¤  A restriction ¤  A conjunction, disjunction or negation of

(sub)queries ¤  Actually there are limitations in the way concepts

can be combined in current facet browser implementations

q = property :value

q1 and q2

q1 or q2

not q


¨  Item description: an information item is described as a conjunction of restrictions

¨  Extension of a query: the set of items in a context O that match the query extO q( ) = o∈O | d o( ) q{ }

ext q1 and q2( )⊆ ext q1( ), ext q2( )ext q1( ), ext q2( )⊆ ext q1 or q2( )ext not q( ) ≡ ext ALL( ) ext q( )

d o( ) = subject :"horse"and style :"Impressionism"and…

o∈O

tc tp ⇒ ext tc( )⊆ ext tp( )


¨  The result of a query is: ¤  Its extension in the given information space ¤ 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( )

extO q( )

Query transformations 36

¨  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.

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How values are (usually) combined ¨  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)

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Type of facets

¨  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

Type of facet values ¨  Terms (strings of text)

¤  Taxonomies, controlled vocabularies

¤  User-defined tags (folksonomies)

¤  From data-mining ¨  Numerical values and dates ¨  Boolean values (yes/no)

¤  E.g. “Available for buying?”, “original?”, “still living?”

¨  Even shades of color, shapes, etc...

¨  Sortable and comparable? ¤  We can say that

value1<=value2<=…<=valueN? ¤  E.g. Dates, magnitudes, scales of

judgment, quantitative data n  e.g. “sufficient”<“excellent”,

10€<100€, “Monday”<“Friday” ¤  Ranges [value1, value2]

n  E.g. User is allowed to search for events from 01/06 to 31/08

¤  Classes of values n  e.g. for price: 0-10€, 11-20€,

21-50€, 51-100€, … n  The way we define classes is arbitrary

and depend on domain

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Benefits of faceted search 40

¨  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

Limitations 41

¨  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

Advanced (research) issues 42

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Full Boolean queries | 1 ¨  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”

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Full Boolean queries | 2 ¨  Foci (Ferré et al.) the set of sub-expressions in the semantic

tree of the query ¨  A query is a pair , where is an arbitrary combination of

filters and 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

q,φ( ) qφ

Semantic faceted search 45

¨  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 (http://www.freebase.com/labs/parallax/) ¤  Challenges: effective models and usable interface

¨  An example: Sewelis

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


î  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


î Monna Lisa is a well known portait of a woman, but…

î There is also a landscape in the background

î Do we classifity it as “subject: woman” and “subject: Tuscan landscape” too?


î Onion is very used in French cuisine

î How do we distinguish “onion-based” recipes from all the recipes with onion inside?


¨  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


¨  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


¨  Interaction (concepts)

Handling information overload 53

¨  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… :-)

Interested in MS Theses? Contact us! :-) ¨  Advisors: Prof. Di Blas, Prof. Paolini ¨  Both theoretical and development ¨  Fuzzy facets ¨  Semantic faceted search ¨  Advanced visualizations ¨  … ¨  Your own ideas! :-)

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Are you still alive/awake? Thank you for your attention!

Any final questions? 55

Education

Models and interaction mechanisms for exploratory interfaces