jane reid, amsc iric, qmul, 13/11/01 1 ir interfaces purpose: to support users in...

Jane Reid, AMSc IRIC, QMUL, 13/11/01

1

IR interfaces

• Purpose: to support users in information-seeking tasks

• Issues:– Functionality

– Usability

• Motivations for interface design:– General interface design principles

– Visualisation

– Need to provide support for information-seeking process itself


2

Interface design principles

• Particularly relevant to IR interfaces:– Reduce working memory load

– Informative feedback

– Internal locus of control

– Alternative interfaces for expert/novice users

– Easy reversal of actions

[Schneiderman]


3

Visualisation [1]

• Visual representation of large information spaces

• 2D / 3D representations

• Difficulty representing:– Abstract ideas

– Textual information

• Techniques:– Graphical

• E.g. icons, colour highlighting

– Brushing and linking• Connection of 2 or more views of the same data


4

Visualisation [2]

– Panning and zooming• Scanning sideways and focussing in / out

– Focus-plus-context• Makes focus area larger and shrinks surrounding objects, e.g. fisheye

view

– Magic lenses• Use of a transparent overlaid window which transforms the

underlying data

– Animation


5

Support for information-seeking process

• Interface must support:– Starting-point

– Query specification

– Presentation of results

– Relevance feedback

– IS process as a whole


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Starting-point

• Lists

• Overviews

• Examples

• Automated source selection


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Lists

• User chooses from list of collection names to search

• No other collection information, so:– Efficient for:

• Frequent searchers

• Domain experts

– Not efficient for:• Infrequent searchers

• Domain novices


8

Overviews

• Overview of topic domains of collections

• Used in combination with navigation

• Three main types:– Category hierarchies

– Automatic collection overviews

– Co-citation clustering


9

Category hierarchies

• Structured overview of topic categories

• Uses manually assigned category labels

• Provides logical high-level starting-point

• Disadvantages:– Category contents not always intuitive

– Difficult to combine categories and queries

• E.g. Yahoo! directory


10

Automatic collection overviews

• Usually based on unsupervised clustering

• Combination with searching facility is most effective

• Often used with graphical display to support browsing

• Disadvantages:– Differences in cohesion of categories

– Graphical display:• Is difficult for non-expert users

• Does not support focussed search well

• E.g. Scatter/Gather


11

Co-citation clustering

• Clustering by citation analysis:– Pairing of documents which

• Both cite the same article

• Are both cited by the same article

– Documents clustered on the basis of co-citation similarity

• Identifies dominant themes in the collection

• Disadvantages:– Differences in cohesion of categories


12

Examples [1]

• Initial example provided by the system

• Retrieval by reformulation

• Methods of choosing initial examples:– Template provided and partially completed -> partial matching

– Case-based reasoning, according to general interests

• Dialogue systems– System-user question-answer


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Examples [2]

• Wizards:– Step-by-step short-cuts for certain tasks

– Useful for:• Multi-step, fixed-sequence tasks

• Users lacking domain knowledge

• Possible future strategy: guided tour (static or dynamic)


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Automated source selection

• User modelling systems

• Intelligent tutoring systems

• Matching on the basis of:– Query / user profile

– Query / contents of information sources

• Alternative strategy: data fusion


15

Query specification

• For different functionality:– Boolean queries

– Free-text queries

– Non-textual

• For different interface styles:– Command line

– Forms / menus

– Graphical


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Boolean queries [1]

• Boolean queries problematic because:– Basic syntax is not intuitive

– Size of results set often unsuitable or unworkable

– Documents are not ranked

• Solutions:– Alternative, simpler syntax

– Faceted queries:• Query divided into facets, which are treated as separate queries

• Results sets combined

• Facets can be weighted to reflect importance


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Boolean queries [2]

– Post-coordinate ranking• Documents ranked by proportion of query terms contained within

them

– Meta-data ranking• Documents ranked by meta-data, e.g. date order, author name


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Free-text queries [1]

• Natural language is a more intuitive method of query specification

• Generally treated as a “bag” of words

• Statistical ranking used

• Disadvantages:– Less feedback about occurrence of terms in the results

– Less control over the results


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Free-text queries [2]

• Variations:– Use of “mandatory” terms

– Use of phrases and term proximity

– Extraction of concepts

– Use of natural language syntax• e.g. if a person, date, place is required

– Question-answering systems:• FAQ systems

• Question template systems


20

Forms / menus

• Command and attribute information provided

• Recognition instead of recall

• Easier for non-expert and infrequent users


21

Graphical interfaces

• Often faster and more accurate for users

• Provides direct manipulation:– Continuous representation of current object

– Physical actions

– Rapid incremental reversible operations

• Examples:– Venn diagrams

– Filter-flow model (DB queries only)

– Query preview

• Possible future strategy: magic lenses


22

Presentation of results [1]

• Document surrogates used

• Show context for current document set:– Query terms within document:

• Highlight query term occurrences

• System scrolls to first query term occurrence

– Query terms between documents• Overview of retrieved documents organised by subset of query terms

contained within them

– Context via table of contents• Context organised into TOC with sections


23

Presentation of results [2]

– Categories for results set context• Documents put into relevant meta-data categories

– Hyperlinks to organise retrieval results• Manually vs automatically generated

– Tables• Positioning documents in table arranged according to 2 or more

variables


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Relevance feedback [1]

• Method of query reformulation

• Different functionality:– Standard relevance feedback (automatic)

• Binary / scalar / negative

– Interactive relevance feedback• Possible query expansion terms offered to user

– Pseudo-relevance feedback• Query expansion terms extracted from top-ranked documents


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Relevance feedback [2]

• Recommender systems:– Machine learning techniques

– Implicit / explicit user input

– Building up / modifying user profile

• Social recommendation systems:– Similarity between different users’ queries and relevance

judgements exploited in order to identify other potential matches

– Effective for “taste” activities, e.g. music, films


26

IS process as a whole [1]

• Layout of information on the screen

• Window management:– Content

– Layout:• Monolithic

• Overlapping

• Elastic windows

• Tiled

• Virtual workspaces


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IS process as a whole [2]

• Search history

• Integration of:– Scanning

– Selection

– Querying


28

Summary

• Purpose of IR interfaces: to support users in information-seeking tasks

• IR interface design deals with issues of:– Functionality

– Usability

• IR interface design takes account of:– General interface design principles

– Visualisation principles and techniques

– Stages of the information-seeking process

jane reid, amsc iric, qmul, 13/11/01 1 ir interfaces purpose: to support users in...

Documents

jane reid

amsc iric

data slide

scattergather slide

collection information

yahoo directory slide

collection disadvantages

process interface