chapter14-seweèpfkpeofspelkmarchinterfaces

8/9/2019 Chapter14-SewepfkpeofspelkmarchInterfaces

1/53

Search Process And InterfacesChapter 14Search Process and Interfaces


2/53

Chapter 14Web Information Retireval

Exploratory Search 2

User intent taxonomy (Broder2002)

Informational

want to learn about something (~40% / 65%)

Navigationalwant to go to a given page (~25% / 15%)

Transactional want to do something (web-mediated) (~35% / 20%)

Exploratory Search belongs to all cases

[W]hen an individual requires any information tocomplete a task, or to satisfy the curiosity of the mind,independent of the method used to address the need,and regardless of whether the need is satisfied or not.

Outline

Information Seeking Paradigms

Search User Interfaces


3/53


Models of Information Seeking Process

How humans search for information?

Information-seeking is a special case of problem solving.It includes recognizing and interpreting the informationproblem, establishing a plan of search, conducting thesearch, evaluating the results, and if necessary, iteratingthrough the process again.

G. Marchionini. Information-seeking strategies of novices using a full-text

electronic encyclopedia.

3


4/53


The Search Process

A hierarchy of goals and tasks

4

Working Context

Information Seeking Context

Information Retrieval

Context

SeekingTask

Work Task

Seeking

Process

Work

Process

SeekingResult

Work Result

InformationNeed

Query

Result

Match


5/53


Information Seeking [Bates, 2002]

Bates, Marcia J. 2002. Toward an integrated model for information seeking and searching. In: The

Fourth International Conference on Information Needs, Seeking and Use in Different Contexts.

5


6/53


The Standard Retrieval Interaction Model

Assumptions: The goal is maximizing

precisionand recallsimultaneously

The information need remainsstatic

The value is in the resultingdocument set

Problems: Users learn during the search

process: Scanning titles of retrieved

documents Reading retrieved documents

Viewing lists of relatedtopics/thesaurus terms

Navigating hyperlinks Some users dont like long

(apparently) disorganized lists ofdocuments

http://courses.sims.berkeley.edu/i240/s11/

6


7/53Chapter 14Web Information Retireval

IR is an Iterative Process

The exchange doesnt end

with first answer Users can recognize elements

of a useful answer, evenwhen incomplete

Questions and understandingchanges as the processcontinues

Repositories

Workspace

Goals

http://courses.sims.berkeley.edu/i240/s11/

7



IR is an Iterative Process

Search is evolving Content Vs. Intent

People dont want to search People want to get task done and get answers

Moving towards identifying a users task

Enabling means for task completion

Search as a Process

Start End

I am craving for a good Wiener Schnitzeland a Sachertorte in Vienna

Search Menu Reviews MapRicardo Baeza-Yates

Next Generat ion Search, 2nd

SeCo Workshop,

Milan, 24/06/2010

Search applications must Support the user in the search process (try to) Infer the user intent to help him accomplishing his

task

8


9/53



Moving between patches

Patches of information = websites

Problem:should I continue foraging in the currentpatchor look for another patch? Expected gain from continuing in current patch vs. moving to

another

10



Information seeking funnel

[D. Rose, 2008]

Wandering: the user does not have aninformation seeking-goal in mind.

Exploring: the user has a general goalbut not a plan for how to achieve it.

Seeking: the user has started to identifyinformation needs that must be satisfiedbut the needs are open-ended.

Asking: the user has a very specificinformationneed that corresponds to a

closed-class question

11



KuhlthausInformation Search Process (ISP) 12



Berrypicking vs. Orienteering vs.

Teleporting ...

Information needschange during interactions

M.J. Bates. The design ofbrowsing and berrypickingtechniques for the onlinesearch interface. OnlineReview, 13(5):407431,1989.

Orienteering[Teevan et al., CHI 2004]: Searcher issues a quick,imprecise to get to approximately the right information space region

and then follows known paths that require small steps that movethem closer to their goal. Easy! (perfect query not needed)

Teleporting: Expert searchers issue longer queries to jump directlyto the target. Requires more effort and experience.

Q0

Q1

Q2

Q3

Q4

Q5

EXIT

TT

T

T

T = thoughtQ = query variation

= document, information

13



vs. exploratory search

Exploratory Search: users intent isprimarily to learn more on a topic of interest,

by exploring various directions and sources exploratory search blends querying and

browsing strategies and is different fromretrieval that is best served by analyticalstrategies

Marchionini, G. Exploratory search:

from finding to understanding.Communications ACM 49(4): 41-46(2006)

Some references

Definition and analysis of the problem

White, R. W., and Drucker, S. M. Investigating behavioral variability in web search. 16th WWW

Conf. Banff, Canada, 2007)

Complex Search and Exploratory Search

Aula, A., and Russell, D.M. Complex and Exploratory Web Search. ISSS: Information Seeking

Support Systems Workshop Chapel Hill, June 2008)

14



Multi-domain Exploratory Search

search for upcoming concerts close to an attractive

location (like a beach, lake, mountain, natural park, andso on), considering also availability of good, close-byhotels

Current approach the user can adopt: Independently explore search services Manually combine findings

15



Multi-domain Exploratory Search

expandthe search to get information about available

restaurants near the candidate concert locations, newsassociated to the event and possible options to combinefurther events scheduled in the same days and located ina close-by place with respect to the first one

16



The note-taking limit

There is a limit after which the found options need to be

marked down.

17



Liquid Queries

A new paradigm allowing users to formulate and get responsesto multi-domain queries through an exploratory information

seeking approach, based upon structuredinformation sourcesexposed as software services

Compositeanswers obtained by aggregating search results from

various domains

Highlightthe contribution of each search service

Joinof results based on the structural information afforded by thesearch service interfaces

Refinethe user query

Re-shape the result list

Alessandro Bozzon, Marco Brambilla, Piero Fraternali, Stefano Ceri. Liquid Query: multi-domain

exploratory search on the Web. WWW 2010, Raleigh, USA

18



Liquid Queries Definition

Template-based approach

It consists of subsetting and parametrizing the resourcegraph...

Concert

Artist

Exhibition

Restaurant

Hotel

Movie

Metro Station

Theatre

Photo

Landmark

News

...

Piece

...

...

...

...

ShoppingCenter...

...

...

Photo

Concert

Metro Station

Restaurant

ews

Exhibition

Artist

Hotel

= inputs, outputs + GR = global ranking

19



Liquid Queries Definition

And then characterizing the user interaction

Plus: Parametrization of global ranking Data visualization options .. and so on

Photo

Concert

Metro Station

Restaurant

ews

Exhibition

Artist

Hotel

Expand

20



Result Exploration Support

If the current set of combinations is not satisfactory, the user may ask formore values for a service (more one) or for all services (more all)

More concerts, more hotels, or more combinations

Add new information about further domains for selected combinations(expand) Find close-by restaurants or co-located events

Aggregateinformation to ease analysis and readability (clustering,grouping) Group events by venue

Reducethe number of shown items through filtering Total walked distance for the night

Re-order (ranking or sorting) Calculate derived values from existing ones Total walked distance for the night

Alternative data visualization Map, parallel coordinates,

DEMO:

http://demo.search-computing.org

21



Outline

Information Seeking Paradigms

Search User Interfaces

22



Search interfaces

Visualization of search results is a core asset in the searchprocess and is accountable for a big part of the perceived

quality in the user experience

23



What web search engines offer

Snippets

24


25/53


Query Specification 25

Yahoos

Advanced

Search Panel

Googles Image

Query By

Example

Autocorrection Autocompletion and Instant


26/53


Autocorrection, Autocompletion, and Instant

Search 26

AutocompletionAutocorrection

Instant Search


27/53


Result Page Real Estate

More Complete information on one search

Shortcuts

Deep Links

Enhanced

Results

27


28/53


Page Layout Optimization_1

Optimization of the result set layout (and of page space)

28


29/53




29


30/53




DEMO VIDEO: http://youtu.be/GkF36gUP7Ss

30


31/53


Not all results are likely to be reviewed

(Source: iprospect.com WhitePaper_2006_SearchEngineUserBehavior.pdf)

31


32/53


Clicks and views depend on rank

[Joachims et al, 2005]

32


33/53


What are facets?

Like the facets on a diamond, faceted metadata allow

you to look at the same information from different sides.

Faceted metadata have the information organized on

several dimensions

Example:

Book: author, title, year, publisher, subject

Shoe: size, color brand, material, price, style

33


34/53


Why facets?

Dynamic method to retrieve information

Provides analytics and brings intelligence to search

results page

Provides discovery tool and brings serendipity

Leverages an engineering taxonomy Facets are structured around key data fields, including controlled

fields based on classification codes and controlled vocabulary

terms

Facets reveal common threads between search results

34

F d h


35/53


Faceted search

Faceted searchis a popular technique for exploratory

searching of digital documents. It allows user to filterthe data using multiple dimensional features.

The interface of Mspace - http://www.mspace.fm/

35


36/53


37/53


What is faceted search?

Its about

Query

Reformulation!

37


38/53


Data Visualization

to convey information through visual

representations.

Why?

Make decision See data in context Support graphical calculation Find patterns

38


39/53


Examples

The French engineer, Charles Minard (1781-1870), illustrated thedisastrous result of Napoleon's failed Russian campaign of 1812. Thegraph displays several variables in a single two-dimensional image:t the army's location and direction, showing where units split off and rejoined the declining size of the army the low temperatures during the retreat.

Many consider Minard's original the best statistical graphic ever drawn.

http://www.math.yorku.ca/SCS/Gallery/minard/march-animated.gif

39


40/53


Points

Simplest object

Pinpoint a specific locationin space

Can take any simple shape

Not a good option forencoding a series of values

through time

40


41/53


Scatter Plot

Easily represents the

measurements of twovariables

Effectively demonstrate the

existence or lack or arelationship betweenvariables

41


42/53


Examples

http://www.guardian.co.uk/world/datablog/interactive/2010/oct/23/wikileaks-iraq-deaths-map

42


43/53


Examples

http://www.ted.com/talks/hans_rosling_reveals_new_insights_on_poverty.html

43


44/53


Results Visualizations

There are well-established visualization concerns intraditional search interfaces (e.g., simplicity)

Recently, more sophisticated presentation ofresults are emerging, driven by the type (andproperties) of extracted data

44


45/53


Results Visualizations

Vertical search engines also feature newsophisticated approaches to result visualization

Domain knowledge optimizes the display of retrievedresults

Wonderfly trip plannerwww.wonderfly.com

45


46/53


Adequate visualizations

Flexible and dynamic assembly of the resultlayout as in horizontal search engines

Result presentation tuned to the type of objects(images, maps, news) retrieved as in vertical

search engines

Visualization techniques that vary quite radically,e.g., using maps to chart multiple geo-referencedobjects, time lines to convey temporal series, and

ad hoc widgets for multidimensional data

46


47/53


(List) Tabular visualization 47


48/53


Map Visualization 48


49/53


Multi-domain Map Visualization 49


50/53


Atom View 50


51/53


Provenance and Relationships

http://sig.ma/

http://askken.heroku.com/

51


52/53


Parallel Coordinates

http://vis.stanford.edu/protovis/ex/cars.html

52


53/53

Faceted Search

http://vis.stanford.edu/protovis/ex/cars.html

53

chapter14-seweèpfkpeofspelkmarchinterfaces

Documents