IR System Evaluation

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Evaluation

LBSC 796/INFM 718R

Session 5, October 7, 2007

Douglas W. Oard

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Agenda

•Questions=

•

•Evaluation fundamentals

•

•System-ce= ntered strategies

•

•User-cent= ered strategies

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IR as an Empirical Discipline

<= span class=3DBB style=3D'position:absolute;left:-3.82%'>•Formulate= a research question: the hypothesis

<= span class=3DBB style=3D'position:absolute;left:-3.89%'>•Design an experiment to answer the question

<= span class=3DBB style=3D'position:absolute;left:-4.39%'>•Perform t= he experiment

<= span class=3DB1B style=3D'position:absolute;left:-3.7%'>–Compare w= ith a baseline “control”

<= span class=3DBB style=3D'position:absolute;left:-4.14%'>•Does the experiment answer the question?

<= span class=3DB1B style=3D'position:absolute;left:-3.58%'>–Are the = results significant? Or is it just luck?

<= span class=3DBB style=3D'position:absolute;left:-4.39%'>•Report the results!

•

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Evaluation Criteria

&#= 8226;Effectiveness

&= #8211;System-only, human+system

– <= /div>

&#= 8226;Efficiency

–Retrieval time, indexing time, index size

&#= 8226;Usability

&= #8211;Learnability, novice use, expert use

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IR Effectiveness Evaluation

&#= 8226;User-centered strategy

&= #8211;Given several users, and at least 2 retrieval systems

&= #8211;Have each user try the same task on both systems

&= #8211;Measure which system works the “best”

– <= /div>

&#= 8226;System-centered strategy

&= #8211;Given documents, queries, and relevance judgments

&= #8211;Try several variations on the retrieval system

&= #8211;Measure which ranks more good docs near the top

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Good Measures of Effectiveness

<= span class=3DBB style=3D'position:absolute;left:-3.99%'>•Capture s= ome aspect of what the user wants

– <= /div>

<= span class=3DBB style=3D'position:absolute;left:-4.22%'>•Have pred= ictive value for other situations

<= span class=3DB1B style=3D'position:absolute;left:-3.56%'>–Different queries, different document collection

– <= /div>

<= span class=3DBB style=3D'position:absolute;left:-4.43%'>•Easily re= plicated by other researchers

– <= /div>

<= span class=3DBB style=3D'position:absolute;left:-4.43%'>•Easily compared

<= span class=3DB1B style=3D'position:absolute;left:-3.97%'>–Optimall= y, expressed as a single number

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Comparing Alternative Approaches

&#= 8226;Achieve a meaningful improvement

&= #8211;An application-specific judgment call

– <= /div>

&#= 8226;Achieve reliable improvement in unseen cases

&= #8211;Can be verified using statistical tests

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Evolution of Evaluation

<= span class=3DBB style=3D'position:absolute;left:-4.58%'>•Evaluatio= n by inspection of examples

•Evaluatio= n by demonstration

<= span class=3DBB style=3D'position:absolute;left:-4.22%'>•Evaluatio= n by improvised demonstration

<= span class=3DBB style=3D'position:absolute;left:-4.14%'>•Evaluatio= n on data using a figure of merit

•Evaluatio= n on test data

•Evaluatio= n on common test data

<= span class=3DBB style=3D'position:absolute;left:-4.29%'>•Evaluatio= n on common, unseen test data

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Which is the Best Rank Order?

=3D relevant document

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Which is the Best Rank Order?

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IR Test Collection Design

&#= 8226;Representative document collection

&#= 8211;Size, sources, genre, topics, …

&#= 8226;“Random” sample of representative queries

&= #8211;Built somehow from “formalized” topic statements

&#= 8226;Known binary relevance

&= #8211;For each topic-document pair (topic, not query!)

&= #8211;Assessed by humans, used only for evaluation

&#= 8226;Measure of effectiveness

&#= 8211;Used to compare alternate systems

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What is relevance?

measure

degree

dimension

estimate

appraisal

relation

= correspondence

utility

connection

satisfaction

fit

bearing

matching

document

article

textual form

reference

information provided =

fact

query

request

information used

point of view

information need statement

person

judge

user

requester

Information specialist

Tefko Saracevic. (1975) Relevanc= e: A Review of and a Framework for Thinking on the Notion in Information Science. Journal of = the American Society for Information Science, 26(6), 321-343;

Relevance is the

of a

existing between a=

and a

as determined by

Does this help?

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Defining “Relevance”

&#= 8226;Relevance relates a topic and a document

&= #8211;Duplicates are equally relevant by definition

&= #8211;Constant over time and across users

– <= /span>

&#= 8226;Pertinence relates a task and a document

&= #8211;Accounts for quality, complexity, language, …

– <= /span>

&#= 8226;Utility relates a user and a document

&= #8211;Accounts for prior knowledge

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Another View

Relevant

Retrieved

Relevant +

Retrieved

Not Relevant + Not Retrieved

Space of all documents

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Set-Based Effectiveness Measures

&#= 8226;Precision

&= #8211;How much of what was found is relevant?

&= #8226;Often of interest, particularly for interactive searching

&#= 8226;Recall

&= #8211;How much of what is relevant was found?

&= #8226;Particularly important for law, patents, and medicine

&#= 8226;Fallout

&= #8211;How much of what was irrelevant was rejected?

&= #8226;Useful when different size collections are compared

------=_NextPart_01C8093B.4DA02340 Content-Location: file:///C:/359A4715/796s075_files/slide0109.htm Content-Transfer-Encoding: quoted-printable Content-Type: text/html; charset="us-ascii" IR System Evaluation

Effectiveness Measures

Relevant Retrieved

False Alarm

Irrelevant Rejected

Miss

Relevant

Not relevant

Retrieved

Not Retrieved

Doc

Action

User-

Oriented

System-

Oriented

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Single-Figure Set-Based Measures

&#= 8226;Balanced F-measure

&= #8211;Harmonic mean of recall and precision

– <= /div>

• <= /div>

&= #8211;Weakness: What if no relevant documents exist?

•<= /span>

•Cost function

&= #8211;Reward relevant retrieved, Penalize non-relevant

&= #8226; For example, 3R+ - 2N+<= /span>

–Weakness: Hard to normalize, so hard to average

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Single-Valued Ranked Measures

&#= 8226;Expected search length

&= #8211;Average rank of the first relevant document

&#= 8226;Mean precision at a fixed number of documents

&= #8211;Precision at 10 docs is often used for Web search

&#= 8226;Mean precision at a fixed recall level

&= #8211;Adjusts for the total number of relevant docs

&#= 8226;Mean breakeven point

&= #8211;Value at which precision =3D recall

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Automatic Evaluation Model

IR Black Box

Query

Ranked List

Documents

= Evaluation

= Module

Measure of Effectiveness

Relevance Judgments

These are the four things we need!

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Ad Hoc Topics

•In TREC, a statement= of information need is called a topic

Title: Health and Computer Terminals &= #13;

Description: Is it hazardous to the health of individuals to work with computer terminals on a daily basis?

Narrative: Relevant documents would contai= n any information that expands on any physical disorder/problems that may be associated with the daily working with comp= uter terminals. Such things as carpel tunnel, cataracts, and fa= tigue have been said to be = associated, but how widespread a= re these or other problems and what is being done to alleviate any health problems.

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Questions About the Black Box

<= span class=3DBB style=3D'position:absolute;left:-4.26%'>•Example “questions”:

&#= 8211;Does morphological analysis improve retrieval performance? <= /div>

&#= 8211;Does expanding the query with synonyms improve retrieval performance?= 3;

•Corresponding experiments:

<= span class=3DB1B style=3D'position:absolute;left:-3.66%'>–Build a “stemmed” index and compare against “unstemme= d” baseline

<= span class=3DB1B style=3D'position:absolute;left:-3.24%'>–Expand q= ueries with synonyms and compare against baseline unexpanded queri= es

------=_NextPart_01C8093B.4DA02340 Content-Location: file:///C:/359A4715/796s075_files/slide0176.htm Content-Transfer-Encoding: quoted-printable Content-Type: text/html; charset="us-ascii" IR System Evaluation

Measuring Precision and Recall

=3D relevant document

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Assume there are a total of 14 relevant documents

Precision

Recall

Precision

Recall

Hits 1-10

Hits 11-20

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R0lGODlhEwAcAHcAMSH+GlNvZnR3YXJlOiBNaWNyb3NvZnQgT2ZmaWNlACH5BAEAAAAALAAAAAAT ABwAgQAAAAAAALKysgECAwI5jI+Zwu3PDJxOUmovzLqGvn2gN5JlJJ7cuIJt92rxNWNpWU95eKK9 sHsETawA4AYzImWAo+JpaAIKADs= ------=_NextPart_01C8093B.4DA02340 Content-Location: file:///C:/359A4715/796s075_files/slide0177.htm Content-Transfer-Encoding: quoted-printable Content-Type: text/html; charset="us-ascii" IR System Evaluation

Graphing Precision and Recall

•Plot each (recall, precision) point on a graph

•Visually represent the precision/recall tradeoff

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Uninterpolated Average Precision

–Average of precision= at each retrieved relevant document –Relevant documents not retrieved contribute zero to score

=3D relevant document

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Precision

Hits 1-10

Hits 11-20

Assume total of 14 relevant documents: 8 relevant documents not retrieved contribu= te eight zeros

<= b>MAP =3D .2307

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Uninterpolated MAP

1/3=3D0.33

2/7=3D0.29

AP=3D0.31

1/1=3D1.00

2/2=3D1.00

3/5=3D0.60

4/9=3D0.44

AP=3D0.76

1/2=3D0.50

3/5=3D0.60

4/8=3D0.50

AP=3D0.53

MAP=3D0.53

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Visualizing Mean Average Precision

0.0

0.2

0.4

0.6

0.8

1.0

Average Precision=

Topic

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What MAP Hides

Adapt= ed from a presentation by Ellen Voorhees at the University of Maryland, March= 29, 1999

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Why Mean Average Precision?

&#= 8226;It is easy to trade between recall and precision

&#= 8211;Adding related query terms improves recall

&= #8226;But naive query expansion techniques kill precision

&= #8211;Limiting matches by part-of-speech helps precision

&= #8226;But it almost always hurts recall

• <= /div>

&#= 8226;Comparisons should give some weight to both

&= #8211;Average precision is a principled way to do this

&= #8211;More “central” than other available measures

------=_NextPart_01C8093B.4DA02340 Content-Location: file:///C:/359A4715/796s075_files/slide0122.htm Content-Transfer-Encoding: quoted-printable Content-Type: text/html; charset="us-ascii" IR System Evaluation

Systems Ranked by Different Measur= es

Ranked by measure aver= aged over 50 topics

Adapt= ed from a presentation by Ellen Voorhees at the University of Maryland, March= 29, 1999

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Correlations Between Rankings

Kendall’s t computed between pairs of rankings =

Adapt= ed from a presentation by Ellen Voorhees at the University of Maryland, March= 29, 1999

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Relevant Document Density

Adapt= ed from a presentation by Ellen Voorhees at the University of Maryland, March= 29, 1999

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o6anqKmhpaqtrq+weayxtLW2tzOzuLu8vbu6vsHCw6LAxMfIyZKJys3Oz3vG0NPU1TZw2Nna29zd 3t/g4eLj5OXm5+jp6uvs7e7v8PHsCQA7 ------=_NextPart_01C8093B.4DA02340 Content-Location: file:///C:/359A4715/796s075_files/slide0125.htm Content-Transfer-Encoding: quoted-printable Content-Type: text/html; charset="us-ascii" IR System Evaluation

Alternative Ways to Get Judgments<= /div>

&#= 8226;Exhaustive assessment is usually impractical

&= #8211;Topics * documents =3D a large number!

•Pooled assessment leverages cooperative evaluation

&= #8211;Requires a diverse set of IR systems

•Search-guided assessment is sometimes viable

&= #8211;Iterate between topic research/search/assessment

&= #8211;Augment with review, adjudication, reassessment

•Known-item judgments have the lowest cost

&= #8211;Tailor queries to retrieve a single known document

&= #8211;Useful as a first cut to see if a new technique is viable

------=_NextPart_01C8093B.4DA02340 Content-Location: file:///C:/359A4715/796s075_files/slide0027.htm Content-Transfer-Encoding: quoted-printable Content-Type: text/html; charset="us-ascii" IR System Evaluation

Obtaining Relevance Judgments

&#= 8226;Exhaustive assessment can be too expensive

&= #8211;TREC has 50 queries for >1 million docs each year

• <= /div>
&#= 8226;Random sampling won’t work

&= #8211;If relevant docs are rare, none may be found!

• <= /div>
&#= 8226;IR systems can help focus the sample

&= #8211;Each system finds some relevant documents

&#= 8211;Different systems find different relevant documents

&= #8211;Together, enough systems will find most of them

------=_NextPart_01C8093B.4DA02340 Content-Location: file:///C:/359A4715/796s075_files/slide0028.htm Content-Transfer-Encoding: quoted-printable Content-Type: text/html; charset="us-ascii" IR System Evaluation

Pooled Assessment Methodology

= 226;Systems submit top 1000 documents per topic

– <= /div>
= 226;Top 100 documents for each are judged

&= #8211;Single pool, without duplicates, arbitrary order

&= #8211;Judged by the person that wrote the query

– <= /div>
&#= 8226;Treat unevaluated documents as not relevant

– <= /div>
&#= 8226;Compute MAP down to 1000 documents

&= #8211;Treat precision for complete misses as 0.0

------=_NextPart_01C8093B.4DA02340 Content-Location: file:///C:/359A4715/796s075_files/slide0187.htm Content-Transfer-Encoding: quoted-printable Content-Type: text/html; charset="us-ascii" IR System Evaluation

Does pooling work?

&#= 8226;Judgments can’t possibly be exhaustive!

– <= /div>
&#= 8226;This is only one person’s opinion about relevance

• <= /div>
– <= /div>
&#= 8226;What about hits 101 to 1000?

– <= /div>
= 226;We can’t possibly use judgments to evaluate a system that didn’t participate in the evaluation!

It doesn’t matter: rela= tive rankings remain the same!

It doesn’t matter: rela= tive rankings remain the same!

It doesn’t matter: rela= tive rankings remain the same!

Actually, we can!<= /div>

<= /span>

Chris Buckley and Ellen M. Voorh= ees. (2004) Retrieval Evaluation with Incomplete Information. SIGIR 2004.

Ellen Voorhees. (1998) Variation= s in Relevance Judgments and the Measurement of Retrieval Effectiveness. SIGIR 1998.<= /span>

Justin Zobel. (1998) How Reliabl= e Are the Results of Large-Scale Information Retrieval Experiments? SIGIR 1998.

------=_NextPart_01C8093B.4DA02340 Content-Location: file:///C:/359A4715/796s075_files/slide0110.htm Content-Transfer-Encoding: quoted-printable Content-Type: text/html; charset="us-ascii" IR System Evaluation

Lessons From TREC

•I= ncomplete judgments are useful

–If sample is unbiased with respect to systems tested

–

•D= ifferent relevance judgments change absolute score

–B= ut rarely change comparative advantages when averaged

–

•E= valuation technology is predictive

–R= esults transfer to operational settings

Adapt= ed from a presentation by Ellen Voorhees at the University of Maryland, March= 29, 1999

------=_NextPart_01C8093B.4DA02340 Content-Location: file:///C:/359A4715/796s075_files/slide0111.htm Content-Transfer-Encoding: quoted-printable Content-Type: text/html; charset="us-ascii" IR System Evaluation

Effects of Incomplete Judgments

&#= 8226;Additional relevant documents are:

&= #8211;roughly uniform across systems

–highly skewed across topics

•<= /span>

•Systems that don’t contribute to pool get comparable results

Adapt= ed from a presentation by Ellen Voorhees at the University of Maryland, March= 29, 1999

------=_NextPart_01C8093B.4DA02340 Content-Location: file:///C:/359A4715/796s075_files/slide0112.htm Content-Transfer-Encoding: quoted-printable Content-Type: text/html; charset="us-ascii" IR System Evaluation

Inter-Judge Agreement

Adapt= ed from a presentation by Ellen Voorhees at the University of Maryland, March= 29, 1999

Relevance judgments did differ for some topics.

Quantify differences using overlap: overlap scores generally higher than most

previous studies.

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Evaluation

Effect of Different Judgments

Adapt= ed from a presentation by Ellen Voorhees at the University of Maryland, March= 29, 1999

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R0lGODlhSQAwAHcAMSH+GlNvZnR3YXJlOiBNaWNyb3NvZnQgT2ZmaWNlACH5BAEAAAAALAAAAABI ADAAgAAAAP///wI/jI+py+0Po5y02ouz3rz7D4biSJbmiabqyrbuC8fyTNf2jef6zvf+DwwKh8Si 8YhMKpfMpvMJjUqn1OoQsCwAADs= ------=_NextPart_01C8093B.4DA02340 Content-Location: file:///C:/359A4715/796s075_files/slide0114.htm Content-Transfer-Encoding: quoted-printable Content-Type: text/html; charset="us-ascii" IR System Evaluation

Net Effect of Different Judges

•Mean Kendall= t between system rankin= gs produced from different qrel sets: .938

&#= 8226;Similar results held for

&= #8211;Different query sets

&= #8211;Different evaluation measures

&= #8211;Different assessor types

–Single opinion vs. gro= up opinion judgments

Adapt= ed from a presentation by Ellen Voorhees at the University of Maryland, March= 29, 1999

------=_NextPart_01C8093B.4DA02340 Content-Location: file:///C:/359A4715/796s075_files/slide0083.htm Content-Transfer-Encoding: quoted-printable Content-Type: text/html; charset="us-ascii" IR System Evaluation

Statistical Significance Tests

•How sure can you be that an observed difference doesn’t simply r= esult from the particular queries you chose?

System A

0.20

0.21

0.22

0.19

0.17

0.20

0.21

System B

0.40

0.41

0.42

0.39

0.37

0.40

0.41

Experiment 1

Query

1

2

3

4

5

6

7

Average

0.20

0.40

System A

0.02

0.39

0.16

0.58

0.04

0.09

0.12

System B

0.76

0.07

0.37

0.21

0.02

0.91

0.46

Experiment 2

Query

1

2

3

4

5

6

7

Average

0.20

0.40

------=_NextPart_01C8093B.4DA02340 Content-Location: file:///C:/359A4715/796s075_files/slide0083_image102.gif Content-Transfer-Encoding: base64 Content-Type: image/gif R0lGODlhoAECAHcAMSH+GlNvZnR3YXJlOiBNaWNyb3NvZnQgT2ZmaWNlACH5BAEAAAAALAAAAACe AQEAgAAAAAAAAAIajI+py+0Po5z0gYuz3rz7D4biSFbmiabquhQAOx== ------=_NextPart_01C8093B.4DA02340 Content-Location: file:///C:/359A4715/796s075_files/slide0090.htm Content-Transfer-Encoding: quoted-printable Content-Type: text/html; charset="us-ascii" IR System Evaluation

Statistical Significance Testing
System A

0.02

0.39

0.16

0.58

0.04

0.09

0.12

System B

0.76

0.07

0.37

0.21

0.02

0.91

0.46

Query

1

2

3

4

5

6

7

Average

0.20

0.40

Sign Test

+

-

+

-

-

+

-

p=3D1.0

Wilcoxon

+0.74

- 0.32

+0.21

- 0.37

- 0.02

+0.82

- 0.38

p=3D0.9375

0

95% of outcomes

Try so= me out at: http://www.fon.hum.uva.nl/Service/Statistics.html

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How Much is Enough?

&#= 8226;Measuring improvement

&= #8211;Achieve a meaningful improvement

&= #8226;Guideline: 0.05 is noticeable, 0.1 makes a difference

&= #8211;Achieve reliable improvement on “typical” queries
&= #8226;Wilcoxon signed rank test for paired samples

–
&#= 8226;Know when to stop!

&= #8211;Inter-assessor agreement limits max precision

&= #8226;Using one judge to assess the other yields about 0.8

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Recap: Automatic Evaluation

&#= 8226;Evaluation measures focus on relevance

&= #8211;Users also want utility and understandability

•
&#= 8226;Goal is to compare systems

&= #8211;Values may vary, but relative differences are stable

•
&#= 8226;Mean values obscure important phenomena

&= #8211;Augment with failure analysis/significance tests

•

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Automatic Evaluation

Search

Query

Ranked List

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User Studies

Search

Query

Selection

Ranked List

Examination

Document

Document

Query

Formulation

Query Reformulation

and

Relevance Feedback

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User Studies

&#= 8226;Goal is to account for interface issues

&= #8211;By studying the interface component

&= #8211;By studying the complete system

– <= /div>
&#= 8226;Formative evaluation

&= #8211;Provide a basis for system development

– <= /div>
&#= 8226;Summative evaluation

&= #8211;Designed to assess performance

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Questions That Involve Users

<= span class=3DBB style=3D'position:absolute;left:-4.1%'>•Example “questions”:

&#= 8211;Does keyword highlighting help users evaluate document relevance? <= /span>

&= #8211;Is letting users weight search terms a good idea?

•Corresponding experiments:

<= span class=3DB1B style=3D'position:absolute;left:-3.28%'>–Build two different interfaces, one with keyword highlighting, one withou= t; run a user study

–Build two different interfaces, one with term weighting functionality, = and one without; run a user study

–

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Blair and Maron (1985)

&#= 8226;A classic study of retrieval effectiveness

&= #8211;Earlier studies used unrealistically small collections

&#= 8226;Studied an archive of documents for a lawsuit

&= #8211;40,000 documents, ~350,000 pages of text

&= #8211;40 different queries

&= #8211;Used IBM’s STAIRS full-text system

&#= 8226;Approach:

&= #8211;Lawyers wanted at least 75% of all relevant documents

= 211;Precision and recall evaluated only after the lawyers were satisfied with the results

David C. Blair and M. E. Maron. = (1984) An Evaluation of Retrieval Effectiveness for a Full-Text Document-Retrieval System. Communications of the ACM, 28(3), 289--299.

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Blair and Maron’s Results

&#= 8226;Mean precision: 79%

&#= 8226;Mean recall: 20% (!!)

&#= 8226;Why recall was low?

&= #8211;Users can’t anticipate terms used in relevant documents

• <= /div>
&#= 8211;Differing technical terminology

&#= 8211;Slang, misspellings

&#= 8226;Other findings:

&= #8211;Searches by both lawyers had similar performance

&= #8211;Lawyer’s recall was not much different from paralegal’s

“accident” might be referred to as “event”, “incident”, “situation”, “problem,” …

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Quantitative User Studies

&#= 8226;Select independent variable(s)

&= #8211;e.g., what info to display in selection interface

&#= 8226;Select dependent variable(s)

&= #8211;e.g., time to find a known relevant document

&#= 8226;Run subjects in different orders

&= #8211;Average out learning and fatigue effects

&#= 8226;Compute statistical significance

&= #8211;Null hypothesis: independent variable has no effect

&= #8211;Rejected if p<0.05

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Variation in Automatic Measures

&#= 8226;System

&= #8211;What we seek to measure

&#= 8226;Topic

&= #8211;Sample topic space, compute expected value

&#= 8226;Topic+System

&= #8211;Pair by topic and compute statistical significance

&#= 8226;Collection

&= #8211;Repeat the experiment using several collections

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Additional Effects in User Studies<= /div>

&#= 8226;Learning

&= #8211;Vary topic presentation order

• <= /div>
&#= 8226;Fatigue

&= #8211;Vary system presentation order

• <= /div>
&#= 8226;Topic+User (Expertise)

&= #8211;Ask about prior knowledge of each topic

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Presentation Order

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Batch vs. User Evaluations

= 226;Do batch (black box) and user evaluations give the same results? If not, why?

&#= 8226;Two different tasks:

&= #8211;Instance recall (6 topics)

– <= /div>
– <= /div>
&= #8211;Question answering (8 topics)

Andrew Turpin and William Hersh. (2001) Why Batch and User Evaluations Do No Give the Same Results. Proceedings of SIGIR 2001= .

=

What countries import Cuban sugar?

What tropical storms, hurricanes, and typhoons have caused = property damage or loss of life?=

=

Which painting did Edvard Munch complete first, “Vampire” or “Puberty”?

Is Denmark larger or smaller in population than Norway?

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Results

&#= 8226;Compared of two systems:

&= #8211;a baseline system

–an improved system that was provably better in batch evaluations

&#= 8226;Results:

0.41<= /span>

0.06<= /span>

0.27<= /span>

0.24<= /span>

p-value
(paired t-test)

-6%

+32%<= /span>

+15%<= /span>

+18%<= /span>

Change<= /span>

60%

0.354= 4

0.372= 8

0.323= 9

Improve= d

66%

0.269= 6

0.323= 0

0.275= 3

Baselin= e

User accuracy

Batch = MAP

User r= ecall

Batch = MAP

Qu= estion Answering

In= stance Recall

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Example User Study Results

Fα=3D0.= 8

Qualitative User Studies

&#= 8226;Observe user behavior

&#= 8211;Instrumented software, eye trackers, etc.

&#= 8211;Face and keyboard cameras

&#= 8211;Think-aloud protocols

&#= 8211;Interviews and focus groups

&#= 8226;Organize the data

&= #8211;For example, group it into overlapping categories

&#= 8226;Look for patterns and themes

&#= 8226;Develop a “grounded theory”

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Example: Mentions= of relevance criteria by searchers

&n= bsp; 2

&n= bsp; 4

    10 (1.4%)

Novelty

&n= bsp; 0

&n= bsp; 9

    11 (1.6%)

Duration

10

&n= bsp; 1

    14 (2%)

Comprehensibility

&n= bsp; 0

&n= bsp; 5

    16 (2.3%)

Audio/Visual Expression

&n= bsp; 0

&n= bsp; 7

    24 (3.4%)

Emotion

&n= bsp; 0

14

    39 (5.5%)

Richness

234

219

535 (76%)

Topicality

Query

Form.

(N=3D248) =

Relevance = Judgment

(N=3D300) =

Think-Aloud

All

(N=3D703)

Number of Mentions

Relevance Criteria

Thesau= rus-based search, recorded interviews

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Topicality

Total mentions by 8 searchers

Thesau= rus-based search, recorded interviews

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O7zbkwAAOw== ------=_NextPart_01C8093B.4DA02340 Content-Location: file:///C:/359A4715/796s075_files/slide0095.htm Content-Transfer-Encoding: quoted-printable Content-Type: text/html; charset="us-ascii" IR System Evaluation

Questionnaires

&#= 8226;Demographic data

&= #8211;For example, computer experience

&= #8211;Basis for interpreting results

– <= /div>
&#= 8226;Subjective self-assessment

&= #8211;Which did they think was more effective?

&= #8211;Often at variance with objective results!

• <= /div>
&#= 8226;Preference

&= #8211;Which interface did they prefer? W= hy?

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Summary

<= span class=3DBB style=3D'position:absolute;left:-3.85%'>•Qualitati= ve user studies suggest what to build

<= span style=3D'visibility:hidden'>•

<= span class=3DBB style=3D'position:absolute;left:-4.18%'>•Design de= composes task into components

<= span style=3D'visibility:hidden'>•

<= span class=3DBB style=3D'position:absolute;left:-3.52%'>•Automated evaluation helps to refine components

<= span style=3D'visibility:hidden'>•

<= span class=3DBB style=3D'position:absolute;left:-3.55%'>•Quantitat= ive user studies show how well it works

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One Minute Paper

•If I demonstrated a = new retrieval technique that achieved a statistically significa= nt improvement in average precision on the TREC collection, what would be the most serious limitation to consider when interpreting that result?

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Click to edit Master text styles

Second level

Third level

Fourth level

Fifth level

IR isn’t very different fr= om other fields we think of as “experimental”, e.g., a chemist in the l= ab mixing bubbling test tubes

Relevance judgments did differ for some topics.

Quantify differences using overlap: overlap scores generally higher t= han most

previous studies.

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event.keyCode =3D=3D 8 ) { event.returnValue =3D 0; parent.GoToPrevSld(); } }function DocumentOnClick() { if( IsNts() || parent.HideMenu() ) return; if( ( g_allowAdvOnClick && (window.name=3D=3D"PPTSld") && !parent.IsFrames= Mode() ) || (event && event.keyCode=3D=3D32) ) { =09 if( g_animUseRuntime && g_animMainSequence && g_animMainSequence.cangonex= t ) return; parent.GoToNextSld(); } } var g_supportsPPTHTML =3D SupportsPPTHTML(), g_scaleInFrame =3D 1, gId=3D""= , g_bgSound=3D"", g_scaleHyperlinks =3D false, g_allowAdvOnClick =3D 1, g_showInBrowser = =3D 0, gLoopCont =3D 0, gUseSldTimings =3D 0; var g_showAnimation =3D g_supportsPPTHTML && SupportsPPTAnimation() && ( (w= indow.name=3D=3D"PPTSld" && !parent.IsFramesMode()) || g_showInBrowser );va= r g_animManager =3D null; var g_animUseRuntime =3D false; var g_animItemsToHide, g_animInteractiveItems, g_animSlideTime; var g_animMainSequence =3D null; var ENDSHOW_MESG=3D"End of slide show, click to exit.", SCREEN_MODE=3D"Fram= es", gIsEndShow=3D0, NUM_VIS_SLDS=3D63, SCRIPT_HREF=3D"script.js", FULLSCR_= HREF=3D"fullscreen.htm"; var gCurSld =3D gPrevSld =3D 1, g_offset =3D 0, gNtsOpen =3D gHasNts =3D gO= tlTxtExp =3D 0, gHasNarration =3D 0, gOtlOpen =3D true window.gPPTHTML=3DSupportsPPTHTML() var gMainDoc=3Dnew Array(new hrefList("slide0001.htm",1,-1,1),new hrefList(= "slide0002.htm",1,-1,1),new hrefList("slide0151.htm",1,-1,1),new hrefList("= slide0033.htm",1,-1,1),new hrefList("slide0011.htm",1,-1,1),new hrefList("s= lide0013.htm",1,-1,1),new hrefList("slide0069.htm",1,-1,1),new hrefList("sl= ide0081.htm",1,-1,1),new hrefList("slide0171.htm",1,-1,1),new hrefList("sli= de0012.htm",1,-1,1),new hrefList("slide0014.htm",1,-1,1),new hrefList("slid= e0162.htm",1,-1,1),new hrefList("slide0101.htm",1,-1,1),new hrefList("slide= 0173.htm",1,-1,1),new hrefList("slide0015.htm",1,-1,1),new hrefList("slide0= 109.htm",1,-1,1),new hrefList("slide0054.htm",1,-1,1),new hrefList("slide00= 19.htm",1,-1,1),new hrefList("slide0169.htm",1,-1,1),new hrefList("slide018= 4.htm",1,-1,1),new hrefList("slide0152.htm",1,-1,1),new hrefList("slide0176= .htm",1,-1,1),new hrefList("slide0177.htm",1,-1,1),new hrefList("slide0182.= htm",1,-1,1),new hrefList("slide0105.htm",1,-1,1),new hrefList("slide0076.h= tm",1,-1,1),new hrefList("slide0117.htm",1,-1,1),new hrefList("slide0022.ht= m",1,-1,1),new hrefList("slide0122.htm",1,-1,1),new hrefList("slide0123.htm= ",1,-1,1),new hrefList("slide0120.htm",1,-1,1),new hrefList("slide0125.htm"= ,1,-1,1),new hrefList("slide0027.htm",1,-1,1),new hrefList("slide0028.htm",= 1,-1,1),new hrefList("slide0187.htm",1,-1,1),new hrefList("slide0110.htm",1= ,-1,1),new hrefList("slide0111.htm",1,-1,1),new hrefList("slide0112.htm",1,= -1,1),new hrefList("slide0113.htm",1,-1,1),new hrefList("slide0114.htm",1,-= 1,1),new hrefList("slide0083.htm",1,-1,1),new hrefList("slide0090.htm",1,-1= ,1),new hrefList("slide0107.htm",1,-1,1),new hrefList("slide0108.htm",1,-1,= 1),new hrefList("slide0148.htm",1,-1,1),new hrefList("slide0149.htm",1,-1,1= ),new hrefList("slide0094.htm",1,-1,1),new hrefList("slide0153.htm",1,-1,1)= ,new hrefList("slide0202.htm",1,-1,1),new hrefList("slide0203.htm",1,-1,1),= new hrefList("slide0097.htm",1,-1,1),new hrefList("slide0134.htm",1,-1,1),n= ew hrefList("slide0135.htm",1,-1,1),new hrefList("slide0136.htm",1,-1,1),ne= w hrefList("slide0204.htm",1,-1,1),new hrefList("slide0205.htm",1,-1,1),new= hrefList("slide0126.htm",1,-1,1),new hrefList("slide0096.htm",1,-1,1),new = hrefList("slide0143.htm",1,-1,1),new hrefList("slide0144.htm",1,-1,1),new h= refList("slide0095.htm",1,-1,1),new hrefList("slide0145.htm",1,-1,1),new hr= efList("slide0124.htm",1,-1,1)); /********************************************* Frameset functions These functions control slide navigation and state of the frameset. **********************************************/ function FullScrInit() { g_allowAdvOnClick =3D GetCurSld().mAdvOnClk document.body.style.backgroundColor=3D"black" document.oncontextmenu=3Dparent._CM; document.onkeydown =3D _KDH; document.ondragstart=3DCancel document.onselectstart=3DCancel self.focus() } function Redirect( frmId ) {=09 var str=3Ddocument.location.hash,idx=3Dstr.indexOf('#'), sId=3DGetSldId() if(idx>=3D0) str=3Dstr.substr(1); if( window.name !=3D frmId && ( sId !=3D str) ) { obj =3D GetObj("Main-File") window.location.href=3Dobj.href+"#"+sId return 1 } return 0 } var MHTMLPrefix =3D CalculateMHTMLPrefix();=20 function CalculateMHTMLPrefix() { if ( document.location.protocol =3D=3D 'mhtml:') {=20 href=3Dnew String(document.location.href)=20 Start=3Dhref.indexOf('!')+1=20 End=3Dhref.lastIndexOf('/')+1=20 if (End < Start)=20 return href.substring(0, Start)=20 else=20 return href.substring(0, End)=20 } return ''; } function GetTags(base,tag) { if(g_supportsPPTHTML) return base.all.tags(tag); else return base.getElementsByTagName(tag); } function UpdNtsPane(){ if(frames["PPTNts"]) PPTNts.location.replace( MHTMLP= refix+GetHrefObj( gCurSld ).mNtsHref ) } function UpdNavPane( sldIndex ){ if(gNavLoaded) PPTNav.UpdNav() } function UpdOtNavPane(){ if(gOtlNavLoaded) PPTOtlNav.UpdOtlNav() } function UpdOtlPane(){ if(gOtlLoaded) PPTOtl.UpdOtl() } function SetHasNts( fVal ) { if( gHasNts !=3D fVal ) { gHasNts=3DfVal UpdNavPane() } } function ToggleOtlText() { gOtlTxtExp=3D!gOtlTxtExp UpdOtlPane() } function ClearMedia() { // Clear any sounds playing before launching another browser window. Other= wise, // in fullscreen mode, you'll continue to hear the sound in the frames mod= e. if (PPTSld.pptSound) PPTSld.pptSound.loop =3D 0; } function FullScreen() {=20 if ( PPTSld.g_animUseRuntime ) PPTSld.document.body.pause(); ClearMedia(); var href =3D ( document.location.protocol =3D=3D 'mhtml:') ? FULLSCR_HREF = : FULLSCR_HREF+"#"+GetHrefObj(gCurSld).mSldHref; if(PPTNav.event.ctrlKey) { var w =3D (window.screen.availWidth * 1.0) / 2.0 var h =3D w * (PPTSld.g_origH * 1.0) / PPTSld.g_origW win =3D window.open( MHTMLPrefix+href,null,"toolbar=3D0,resizable=3D1,top= =3D0,left=3D0," + "width=3D"+ w + ",height=3D" + h ); if( win.document.body && PPTSld.g_animUseRuntime ) win.document.body.PPTSldFrameset=3Dwindow; } else { win =3D window.open( MHTMLPrefix+href,null,"fullscreen=3Dyes" ); if( win.document.body && PPTSld.g_animUseRuntime ) win.document.body.PPTSldFrameset=3Dwindow; } } function ToggleVNarration() { rObj=3DPPTSld.document.all("NSPlay") if( rObj && !PPTSld.g_animUseRuntime ) { if( (rObj.playState =3D=3D 1)||(rObj.playState =3D=3D 0) ) rObj.Play() else if( rObj.playState =3D=3D 2 ) rObj.Pause() else return; } else if( PPTSld.g_animUseRuntime ) { narObj =3D PPTSld.document.all("narrationID") if( narObj ) narObj.togglePause() } } function GetCurSldNum() { =20 obj=3DGetHrefObj(gCurSld) if( obj.mOrigVis =3D=3D 1 ) return obj.mSldIdx else =20 return gCurSld } function GetNumSlds() { =20 if( GetHrefObj(gCurSld).mOrigVis =3D=3D 1 ) return GetSldList().mNumVisSlds; else return GetSldList().mList.length } function GetSldNum( href ) { for(ii=3D0; ii 1 ) PopSldList(); else if( !IsFramesMode() ) { if( gLoopCont ) GoToFirst() else EndShow() } } function GoToPrevSld() { ii=3DgCurSld-1 if( ii > 0 ) { obj=3DGetHrefObj(ii) while ( obj && ( obj.mVis =3D=3D 0 ) && ( ii>0 ) ) obj=3DGetHrefObj(--ii) if( ii =3D=3D 0 ) ii=3D1 GoToSldNum(ii) } } function GoToFirst(){ GoToSld( GetHrefObj(1).mSldHref ) } function GoToLast() { ii=3DGetSldList().mList.length if( ii !=3D gCurSld ) GoToSld( GetHrefObj(ii).mSldHref ) } function GoToSldNum( num ) { if( PPTSld.event ) PPTSld.event.cancelBubble=3Dtrue obj =3D GetHrefObj( num ) obj.mVis=3D1 gPrevSld=3DgCurSld gCurSld =3D num; PPTSld.location.replace(MHTMLPrefix+obj.mSldHref) if( IsFramesMode() ) { UpdNavPane(); UpdOtlPane(); UpdNtsPane() } } function GoToSld( href ) { if( PPTSld.event ) PPTSld.event.cancelBubble=3Dtrue GetHrefObj( GetSldNum(href) ).mVis=3D1 PPTSld.location.replace(MHTMLPrefix+href) } function SldUpdated( id ) { if( id =3D=3D GetHrefObj(gCurSld).mSldHref ) return gPrevSld=3DgCurSld gCurSld=3DGetSldNum(id) if( IsFramesMode() ) { UpdNavPane(); UpdOtlPane(); UpdNtsPane() } } function PrevSldViewed(){ GoToSld( GetHrefObj(gPrevSld).mSldHref ) } function HasPrevSld() { return ( gIsEndShow || ( gCurSld !=3D 1 && GetHrefO= bj( gCurSld-1 ).mVis =3D=3D 1 )||( GetCurSldNum() > 1 ) ) } function HasNextSld() { return (GetCurSldNum() !=3D GetNumSlds()) } function CloseWindow() { if( HideMenu() ) return; =09 var event =3D PPTSld.event; if( !IsFramesMode() && event && (event.keyCode=3D=3D27 || event.keyCode=3D= =3D32 || event.type=3D=3D"click" ) ) window.close( self ); CatchNumKeys( self, event ); } function Unload() { gIsEndShow=3D0; } function SetupEndShow() { gIsEndShow=3D1; PPTSld.document.body.scroll=3D"no"; PPTSld.document.onkeypress=3DCloseWindow; PPTSld.document.onclick=3DCloseWindow; PPTSld.document.oncontextmenu=3D_CM; } function EndShow() { if( IsFramesMode() ) return if( PPTSld.event ) PPTSld.event.cancelBubble=3Dtrue doc=3DPPTSld.document var dir =3D doc.body.dir if( dir !=3D "rtl" ) dir =3D "ltr"; doc.open() doc.writeln('

' + ENDSHOW_MESG + '
') doc.close() } function SetSldVisited(){ GetSldList().mList[gCurSld-1].mVisited=3Dtrue } function IsSldVisited(){ return GetSldList().mList[gCurSld-1].mVisited } function hrefList( sldHref, visible, advDelay, advClk ) { this.mSldHref=3D this.mNtsHref =3D sldHref this.mOrigVis=3D this.mVis =3D visible this.mVisited=3D false this.mAdvDelay=3D advDelay this.mAdvOnClk=3D advClk } function SldList(arr,curSld,fEnd) { this.mCurSld =3D curSld; this.mList =3D new Array(); var idx =3D 1; for(ii=3D0;ii 0) { PushSldList(sldList,fEnd); gCurSld =3D 1; } else if( PPTSld.event ) PPTSld.event.cancelBubble=3Dtrue } function PushSldList(arr,fEnd) { var ii =3D gSldStack.length; gSldStack[ii] =3D new SldList(arr,gCurSld,fEnd); GoToSld( gSldStack[ii].mList[0].mSldHref ); } function PopSldList() { if (gSldStack[gSldStack.length-1].fEndShow) EndShow() else { gCurSld =3D gSldStack[gSldStack.length-1].mCurSld; gSldStack[gSldStack.length-1] =3D null; gSldStack.length--; var sldList =3D gSldStack[gSldStack.length-1]; GoToSld( sldList.mList[gCurSld - 1].mSldHref ); } } var custShowList=3Dnew Array(); /********************************************* Navigation button implementation There are 2 types of buttons: ImgBtn, TxtBtn implemented as function objects. They share a similiar interface so the event handlers can call SetActive, for example, on a button=20 object without needing to know exactly=20 what type of button it is. **********************************************/ //---------------------------------- function ImgBtn( oId,bId,w,action ) //---------------------------------- { var t=3Dthis t.Perform =3D _IBP t.SetActive =3D _IBSetA t.SetInactive=3D _IBSetI t.SetPressed =3D _IBSetP t.SetDisabled=3D _IBSetD t.Enabled =3D _IBSetE t.ChangeIcon =3D null t.UserAction =3D action t.ChgState =3D _IBUI t.mObjId =3D oId t.mBorderId=3D bId t.mWidth =3D w t.mIsOn =3D t.mCurState =3D 0 } function _IBSetA() { if( this.mIsOn ) { obj=3Dthis.ChgState( gHiliteClr,gShadowClr,2 ) obj.style.posTop=3D0 } } function _IBSetI() { if( this.mIsOn ) { obj=3Dthis.ChgState( gFaceClr,gFaceClr,1 ) obj.style.posTop=3D0=20 } } function _IBSetP() { if( this.mIsOn ) { obj=3Dthis.ChgState( gShadowClr,gHiliteClr,2 ) obj.style.posLeft+=3D1; obj.style.posTop+=3D1 } } function _IBSetD() { =20 obj=3Dthis.ChgState( gFaceClr,gFaceClr,0 ) obj.style.posTop=3D0=20 } function _IBSetE( state ) { var t=3Dthis GetObj( t.mBorderId ).style.visibility=3D"visible" if( state !=3D t.mIsOn ) { t.mIsOn=3Dstate if( state ) t.SetInactive() else t.SetDisabled() } } function _IBP() { var t=3Dthis if( t.mIsOn ) { if( t.UserAction !=3D null ) t.UserAction() if( t.ChangeIcon ) { obj=3DGetObj(t.mObjId) if( t.ChangeIcon() ) obj.style.posLeft=3Dobj.style.posLeft+(t.mCurState-4)*t.mWidth else obj.style.posLeft=3Dobj.style.posLeft+(t.mCurState-0)*t.mWidth } t.SetActive() } =20 } function _IBUI( clr1,clr2,nextState ) { var t=3Dthis SetBorder( GetObj( t.mBorderId ),clr1,clr2 ) obj=3DGetObj( t.mObjId ) obj.style.posLeft=3Dobj.style.posLeft+(t.mCurState-nextState)*t.mWidth-obj= .style.posTop t.mCurState=3DnextState return obj } //----------------------------------------- function TxtBtn( oId,oeId,action,chkState ) //----------------------------------------- { var t=3Dthis t.Perform =3D _TBP t.SetActive =3D _TBSetA t.SetInactive=3D _TBSetI t.SetPressed =3D _TBSetP t.SetDisabled=3D _TBSetD t.SetEnabled =3D _TBSetE t.GetState =3D chkState t.UserAction =3D action t.ChgState =3D _TBUI t.mObjId =3D oId t.m_elementsId=3D oeId t.mIsOn =3D 1 } function _TBSetA() { var t=3Dthis if( t.mIsOn && !t.GetState() ) t.ChgState( gHiliteClr,gShadowClr,0,0 ) } function _TBSetI() { var t=3Dthis if( t.mIsOn && !t.GetState() ) t.ChgState( gFaceClr,gFaceClr,0,0 ) } function _TBSetP() { if( this.mIsOn ) this.ChgState( gShadowClr,gHiliteClr,1,1 ) } function _TBSetD() { =20 this.ChgState( gFaceClr,gFaceClr,0,0 ) this.mIsOn =3D 0 } function _TBSetE() { var t=3Dthis if( !t.GetState() ) t.ChgState( gFaceClr,gFaceClr,0,0 ) else t.ChgState( gShadowClr,gHiliteClr,1,1 ) t.mIsOn =3D 1 } function _TBP() { var t=3Dthis if( t.mIsOn ) {=20 if( t.UserAction !=3D null ) t.UserAction() if( !t.GetState ) return if( t.GetState() ) t.SetPressed() else t.SetActive() } =20 } function _TBUI( clr1,clr2,lOffset,tOffset ) { SetBorder( GetObj( this.mObjId ),clr1,clr2 ) Offset( GetObj( this.m_elementsId ),lOffset,tOffset ) } function Offset( obj, top, left ){ obj.style.top=3Dtop; obj.style.left=3Dle= ft } function SetBorder( obj, upperLeft, lowerRight ) { s=3Dobj.style; s.borderStyle =3D "solid" s.borderWidth =3D 1=20 s.borderLeftColor =3D s.borderTopColor =3D upperLeft s.borderBottomColor=3D s.borderRightColor =3D lowerRight } function GetBtnObj(){ return gBtnArr[window.event.srcElement.id] } function BtnOnOver(){ b=3DGetBtnObj(); if( b !=3D null ) b.SetActive() } function BtnOnDown(){ b=3DGetBtnObj(); if( b !=3D null ) b.SetPressed() } function BtnOnOut(){ b=3DGetBtnObj(); if( b !=3D null ) b.SetInactive() } function BtnOnUp() { b=3DGetBtnObj() if( b !=3D null ) b.Perform() else Upd() } function GetNtsState(){ return parent.gNtsOpen } function GetOtlState(){ return parent.gOtlOpen } function GetOtlTxtState(){ return parent.gOtlTxtExp } function NtsBtnSetFlag( fVal ) { s=3Ddocument.all.item( this.m_flagId ).style s.display=3D"none" if( fVal ) s.display=3D"" else s.display=3D"none" } function _BSetA_Border(){ b =3D gBtnArr[this.mObjId]; if( b !=3D null ) b.S= etActive() } function _BSetI_Border(){ b =3D gBtnArr[this.mObjId]; if( b !=3D null ) b.S= etInactive() } function _BSetP_Border(){ b =3D gBtnArr[this.mObjId]; if( b !=3D null ) b.S= etPressed() } function _BSetA_BorderImg() {=20 b =3D gBtnArr[this.mBorderId]=20 if( b !=3D null && this.mIsOn && !b.GetState() ) { obj=3Dthis.ChgState( gHiliteClr,gShadowClr,2 ) obj.style.posTop=3D0 } } function _BSetI_BorderImg() {=20 b =3D gBtnArr[this.mBorderId] if( b !=3D null && this.mIsOn && !b.GetState() ) { obj=3Dthis.ChgState( gFaceClr,gFaceClr,1 ) obj.style.posTop=3D0 } } var gHiliteClr=3D"THREEDHIGHLIGHT",gShadowClr=3D"THREEDSHADOW",gFaceClr=3D"= THREEDFACE" var gBtnArr =3D new Array() gBtnArr["nb_otl"] =3D new TxtBtn( "nb_otl","nb_otlElem",parent.ToggleOtlPan= e,GetOtlState ) gBtnArr["nb_otlElem"] =3D new TxtBtn( "nb_otl","nb_otlElem",parent.ToggleOt= lPane,GetOtlState ) gBtnArr["nb_nts"] =3D new ImgBtn( "nb_nts","nb_ntsBorder",10,parent.ToggleN= tsPane ) gBtnArr["nb_nts"].SetActive =3D _BSetA_BorderImg; gBtnArr["nb_nts"].SetInactive =3D _BSetI_BorderImg; gBtnArr["nb_ntsBorder"] =3D new TxtBtn( "nb_ntsBorder","nb_ntsElem",parent.= ToggleNtsPane,GetNtsState ) gBtnArr["nb_ntsElem"] =3D new TxtBtn( "nb_ntsBorder","nb_ntsElem",parent.To= ggleNtsPane,GetNtsState ) gBtnArr["nb_prevBorder"] =3D gBtnArr["nb_prev"]=3D new ImgBtn( "nb_prev","n= b_prevBorder",30,parent.GoToPrevSld ) gBtnArr["nb_nextBorder"] =3D gBtnArr["nb_next"]=3D new ImgBtn( "nb_next","n= b_nextBorder",30,parent.GoToNextSld ) gBtnArr["nb_sldshw"]=3D new ImgBtn( "nb_sldshw","nb_sldshwBorder",18,parent= .FullScreen ) gBtnArr["nb_sldshwBorder"] =3D new TxtBtn( "nb_sldshw","nb_sldshwBorder",pa= rent.FullScreen,null ) gBtnArr["nb_sldshwBorder"].SetActive =3D _BSetA_Border; gBtnArr["nb_sldshwBorder"].SetInactive =3D _BSetI_Border; gBtnArr["nb_sldshwText"] =3D new TxtBtn( "nb_sldshw","nb_sldshwText",parent= .FullScreen,null ) gBtnArr["nb_sldshwText"].SetActive =3D _BSetA_Border; gBtnArr["nb_sldshwText"].SetInactive =3D _BSetI_Border; gBtnArr["nb_voice"] =3D gBtnArr["nb_voiceBorder"] =3D new ImgBtn( "nb_voice= ","nb_voiceBorder",18,parent.ToggleVNarration ) gBtnArr["nb_otlTxtBorder"] =3D gBtnArr["nb_otlTxt"]=3D new ImgBtn( "nb_otlT= xt","nb_otlTxtBorder",23,parent.ToggleOtlText ) gBtnArr["nb_ntsBorder"].m_flagId=3D "nb_nts" gBtnArr["nb_ntsBorder"].SetFlag =3D NtsBtnSetFlag gBtnArr["nb_otlTxt"].ChangeIcon=3D GetOtlTxtState /********************************************* Context menu implementation _CM() is the function that's hooked up to the oncontextmenu event. Once we're asked to show the menu, we first build it by creating DIVs on-the-fly. Then we position it=20 within the screen area so it doesn't get clipped. Creating the DIVs using createElement() means we don't have to write out any extra HTML into the slide HTML files. **********************************************/ var sNext=3D"Next",sPrev=3D"Previous",sEnd=3D"End Show",sFont=3D"Arial",sAr= row=3D"Arrow",sFreeform=3D"Freeform",sRect=3D"Rectangle",sOval=3D"Oval" function ShowMenu() { BuildMenu(); var doc=3DPPTSld.document.body,x=3DPPTSld.event.clientX+doc.scrollLeft,y= =3DPPTSld.event.clientY+doc.scrollTop m =3D PPTSld.document.all.item("ctxtmenu") m.style.pixelLeft=3Dx if( (x+m.scrollWidth > doc.clientWidth)&&(x-m.scrollWidth > 0) ) m.style.pixelLeft=3Dx-m.scrollWidth m.style.pixelTop=3Dy if( (y+m.scrollHeight > doc.clientHeight)&&(y-m.scrollHeight > 0) ) m.style.pixelTop=3Dy-m.scrollHeight m.style.display=3D"" } function _CM() { if( !parent.IsFullScrMode() ) return; if(!PPTSld.event.ctrlKey) { ShowMenu() return false } else HideMenu() } function BuildMenu() { if( PPTSld.document.all.item("ctxtmenu") ) return var mObj=3DCreateItem( PPTSld.document.body ) mObj.id=3D"ctxtmenu" mObj.style.visibility=3D"hidden" var s=3DmObj.style s.position=3D"absolute" s.cursor=3D"default" s.width=3D"120px" SetCMBorder(mObj,"menu","black") var iObj=3DCreateItem( mObj ) SetCMBorder( iObj, "threedhighlight","threedshadow" ) iObj.style.padding=3D2 CreateMenuItem( iObj,sNext,M_GoNextSld,M_True ) CreateMenuItem( iObj,sPrev,M_GoPrevSld,M_HasPrevSld ) =09 CreateSeparator( iObj ) CreateMenuItem( iObj,sEnd,M_End,M_True ) mObj.style.visibility=3D"visible" } function Cancel() { window.event.cancelBubble=3Dtrue; 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No= tes

Slide Show

Outline

1

Evaluation

LBSC 796/INFM 718R

Session 5, October 7, 2007

Douglas W. Oard

2

Agenda

Questions

Evaluation fundamentals

System-centered strategies

User-centered strategies

3

IR as an Empiric= al Discipline

Formulate a research question: the hypothesis

Design an experiment to answer the question

Perform the experiment

Compare with a baseline “control”

Does the experiment answer the question?

Are the results significant? Or is it just luck?

Report the results!

4

Evaluation Crite= ria

Effectiveness

System-only, human+system

Efficiency

Retrieval time, indexing time, index size

Usability

Learnability, novice use, expert use

5

IR Effectiveness Evaluation

User-centered strategy

Given several users, and at least 2 retrieval systems

Have each user try the same task on both systems

Measure which system works the “best”

System-centered strategy

Given documents, queries, and relevance judgments

Try several variations on the retrieval system

Measure which ranks more good docs near the top

6

Good Measures of Effectiveness

Capture some aspect of what the user wants

Have predictive value for other situations

Different queries, different document collection

Easily replicated by other researchers

Easily compared

Optimally, expressed as a single number

7

Comparing Altern= ative Approaches

Achieve a meaningful improvement

An application-specific judgment call

Achieve reliable improvement in unseen cases

Can be verified using statistical tests

8

Evolution of Eva= luation

Evaluation by inspection of examples

Evaluation by demonstration

Evaluation by improvised demonstration

Evaluation on data using a figure of merit

Evaluation on test data

Evaluation on common test data

Evaluation on common, unseen test data

9

Which is the Bes= t Rank Order?

10

Which is the Be= st Rank Order?

11

IR Test Collect= ion Design

Representative document collection

Size, sources, genre, topics, …

“Random” sample of representative queries

Built somehow from “formalized” topic statements

Known binary relevance

For each topic-document pair (topic, not query!)

Assessed by humans, used only for evaluation

Measure of effectiveness

Used to compare alternate systems

12

What is relevan= ce?

13

Defining “Relevance”

Relevance relates a topic and a document

Duplicates are equally relevant by definition

Constant over time and across users

Pertinence relates a task and a document

Accounts for quality, complexity, language, …

Utility relates a user and a document

Accounts for prior knowledge

14

Another View

15

Set-Based Effec= tiveness Measures

Precision

How much of what was found is relevant?

Often of interest, particularly for interactive searching

Recall

How much of what is relevant was found?

Particularly important for law, patents, and medicine

Fallout

How much of what was irrelevant was rejected?

Useful when different size collections are compared

16

Effectiveness M= easures

17

Single-Figure S= et-Based Measures

Balanced F-measure

Harmonic mean of recall and precision

Weakness: What if no relevant documents exist?

Cost function

Reward relevant retrieved, Penalize non-relevant

For example, 3R^=
+- 2N⁺

Weakness: Hard to normalize, so hard to average

18

Single-Valued R= anked Measures

Expected search length

Average rank of the first relevant document

Mean precision at a fixed number of documents

Precision at 10 docs is often used for Web search

Mean precision at a fixed recall level

Adjusts for the total number of relevant docs

Mean breakeven point

Value at which precision =3D recall

19

Automatic Evalu= ation Model

20

Ad Hoc Topics

In TREC, a statement of information need is called a topic

21

Questions About= the Black Box

Example “questions”:

Does morphological analysis improve retrieval performance?

Does expanding the query with synonyms improve retrieval performanc= e?

Corresponding experiments:

Build a “stemmed” index and compare against “unstemmed” baseline

Expand queries with synonyms and compare against baseline unexpanded queries

22

Measuring Preci= sion and Recall

23

Graphing Precis= ion and Recall

Plot each (recall, precision) point on a graph

Visually represent the precision/recall tradeoff

24

Uninterpolated = Average Precision

Average of precision at each retrieved relevant document

Relevant documents not retrieved contribute zero to score

25

Uninterpolated = MAP

26

Visualizing Mean Average Precision

27

What MAP Hides<= /a>

28

Why Mean Average Precision?

It is easy to trade between recall and precision

Adding related query terms improves recall

But naive query expansion techniques kill precision

Limiting matches by part-of-speech helps precision

But it almost always hurts recall

Comparisons should give some weight to both

Average precision is a principled way to do this

More “central” than other available measures

29

Systems Ranked = by Different Measures

30

Correlations Be= tween Rankings

31

Relevant Docume= nt Density

32

Alternative Way= s to Get Judgments

Exhaustive assessment is usually impractical

Topics * documents =3D a large number!

Pooled assessment leverages cooperative evaluation

Requires a diverse set of IR systems

Search-guided assessment is sometimes viable

Iterate between topic research/search/assessment

Augment with review, adjudication, reassessment

Known-item judgments have the lowest cost

Tailor queries to retrieve a single known document

Useful as a first cut to see if a new technique is viable

33

Obtaining Relev= ance Judgments

Exhaustive assessment can be too expensive

TREC has 50 queries for >1 million docs each year

Random sampling won’t work

If relevant docs are rare, none may be found!

IR systems can help focus the sample

Each system finds some relevant documents

Different systems find different relevant documents

Together, enough systems will find most of them

34

Pooled Assessme= nt Methodology

Systems submit top 1000 documents per topic

Top 100 documents for each are judged

Single pool, without duplicates, arbitrary order

Judged by the person that wrote the query

Treat unevaluated documents as not relevant

Compute MAP down to 1000 documents

Treat precision for complete misses as 0.0

35

Does pooling wo= rk?

Judgments can’t possibly be exhaustive!

This is only one person’s opinion about relevance

What about hits 101 to 1000?

We can’t possibly use judgments to evaluate a system that didn’t participate in the evaluation!

36

Lessons From TR= EC

Incomplete judgments are useful

If sample is unbiased with respect to systems tested

Different relevance judgments change absolute score

But rarely change comparative advantages when averaged

Evaluation technology is predictive

Results transfer to operational settings

37

Effects of Inco= mplete Judgments

Additional relevant documents are:

roughly uniform across systems

highly skewed across topics

Systems that don’t contribute to pool get comparable results

38

Inter-Judge Agr= eement

39

Effect of Diffe= rent Judgments

40

Net Effect of D= ifferent Judges

Mean Kendall t between system rankings produced from different qrel sets: .938

Similar results held for

Different query sets

Different evaluation measures

Different assessor types

Single opinion vs. group opinion judgments

41

Statistical Significance Tests

How sure can you be that an observed difference doesn’t simply result from the particular queries you chose?

42

Statistical Significance Testing

43

How Much is Eno= ugh?

Measuring improvement

Achieve a meaningful improvement

Guideline: 0.05 is noticeable, 0.1 makes a difference

Achieve reliable improvement on “typical” queries

Wilcoxon signed rank test for paired samples

Know when to stop!

Inter-assessor agreement limits max precision

Using one judge to assess the other yields about 0.8

44

Recap: Automatic Evaluation

Evaluation measures focus on relevance

Users also want utility and understandability

Goal is to compare systems

Values may vary, but relative differences are stable

Mean values obscure important phenomena

Augment with failure analysis/significance tests

45

Automatic Evalu= ation

46

User Studies

47

User Studies

Goal is to account for interface issues

By studying the interface component

By studying the complete system

Formative evaluation

Provide a basis for system development

Summative evaluation

Designed to assess performance

48

Questions That = Involve Users

Example “questions”:

Does keyword highlighting help users evaluate document relevance?
Is letting users weight search terms a good idea?

Corresponding experiments:

Build two different interfaces, one with keyword highlighting, one without; run a user study

Build two different interfaces, one with term weighting functionali= ty, and one without; run a user study

49

Blair and Maron= (1985)

A classic study of retrieval effectiveness

Earlier studies used unrealistically small collections

Studied an archive of documents for a lawsuit

40,000 documents, ~350,000 pages of text

40 different queries

Used IBM’s STAIRS full-text system

Approach:

Lawyers wanted at least 75% of all relevant documents

Precision and recall evaluated only after the lawyers were satisfied with the results

50

Blair and Maron= ’s Results

Mean precision: 79%

Mean recall: 20% (!!)

Why recall was low?

Users can’t anticipate terms used in relevant documents

Differing technical terminology

Slang, misspellings

Other findings:

Searches by both lawyers had similar performance

Lawyer’s recall was not much different from paralegal’s=

51

Quantitative Us= er Studies

Select independent variable(s)

e.g., what info to display in selection interface

Select dependent variable(s)

e.g., time to find a known relevant document

Run subjects in different orders

Average out learning and fatigue effects

Compute statistical significance

Null hypothesis: independent variable has no effect

Rejected if p<0.05

52

Variation in Au= tomatic Measures

System

What we seek to measure

Topic

Sample topic space, compute expected value

Topic+System

Pair by topic and compute statistical significance

Collection

Repeat the experiment using several collections

53

Additional Effe= cts in User Studies

Learning

Vary topic presentation order

Fatigue

Vary system presentation order

Topic+User (Expertise)

Ask about prior knowledge of each topic

54

Presentation Or= der

55

Batch vs. User Evaluations

Do batch (black box) and user evaluations give the same results? If = not, why?

Two different tasks:

Instance recall (6 topics)

Question answering (8 topics)

56

Results

Compared of two systems:

a baseline system

an improved system that was provably better in batch evaluations

Results:

57

Example User St= udy Results

58

Qualitative User Studies

Observe user behavior

Instrumented software, eye trackers, etc.

Face and keyboard cameras

Think-aloud protocols

Interviews and focus groups

Organize the data

For example, group it into overlapping categories

Look for patterns and themes

Develop a “grounded theory”

59

60

Topicality<= /font>

61

Questionnaires<= /a>

Demographic data

For example, computer experience

Basis for interpreting results

Subjective self-assessment

Which did they think was more effective?

Often at variance with objective results!

Preference

Which interface did they prefer?&n= bsp; Why?

62

Summary

Qualitative user studies suggest what to build

Design decomposes task into components

Automated evaluation helps to refine components

Quantitative user studies show how well it works

63

One Minute Pape= r

If I demonstrated a new retrieval technique that achieved a statistically significant improvement in average precision on the TR= EC collection, what would be the most serious limitation to consider wh= en interpreting that result?

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	IR isn’t very different from other fields we think of as “experimental”, e.g., a chemist in the lab mixing bubbling te= st tubes


	Relevance judgments did differ for some topics.

	Quantify differences using overlap: overlap scores generally higher than most
	previous studies.

1	Evaluation LBSC 796/INFM 718R Session 5, October 7, 2007 Douglas W. Oard
2	Agenda Questions Evaluation fundamentals System-centered strategies User-centered strategies
3	IR as an Empiric= al Discipline Formulate a research question: the hypothesis Design an experiment to answer the question Perform the experiment Compare with a baseline “control” Does the experiment answer the question? Are the results significant? Or is it just luck? Report the results!
4	Evaluation Crite= ria Effectiveness System-only, human+system Efficiency Retrieval time, indexing time, index size Usability Learnability, novice use, expert use
5	IR Effectiveness Evaluation User-centered strategy Given several users, and at least 2 retrieval systems Have each user try the same task on both systems Measure which system works the “best” System-centered strategy Given documents, queries, and relevance judgments Try several variations on the retrieval system Measure which ranks more good docs near the top
6	Good Measures of Effectiveness Capture some aspect of what the user wants Have predictive value for other situations Different queries, different document collection Easily replicated by other researchers Easily compared Optimally, expressed as a single number
7	Comparing Altern= ative Approaches Achieve a meaningful improvement An application-specific judgment call Achieve reliable improvement in unseen cases Can be verified using statistical tests
8	Evolution of Eva= luation Evaluation by inspection of examples Evaluation by demonstration Evaluation by improvised demonstration Evaluation on data using a figure of merit Evaluation on test data Evaluation on common test data Evaluation on common, unseen test data
9	Which is the Bes= t Rank Order?
10	Which is the Be= st Rank Order?
11	IR Test Collect= ion Design Representative document collection Size, sources, genre, topics, … “Random” sample of representative queries Built somehow from “formalized” topic statements Known binary relevance For each topic-document pair (topic, not query!) Assessed by humans, used only for evaluation Measure of effectiveness Used to compare alternate systems
12	What is relevan= ce?
13	Defining “Relevance” Relevance relates a topic and a document Duplicates are equally relevant by definition Constant over time and across users Pertinence relates a task and a document Accounts for quality, complexity, language, … Utility relates a user and a document Accounts for prior knowledge
14	Another View
15	Set-Based Effec= tiveness Measures Precision How much of what was found is relevant? Often of interest, particularly for interactive searching Recall How much of what is relevant was found? Particularly important for law, patents, and medicine Fallout How much of what was irrelevant was rejected? Useful when different size collections are compared
16	Effectiveness M= easures
17	Single-Figure S= et-Based Measures Balanced F-measure Harmonic mean of recall and precision Weakness: What if no relevant documents exist? Cost function Reward relevant retrieved, Penalize non-relevant For example, 3R^= +- 2N⁺ Weakness: Hard to normalize, so hard to average
18	Single-Valued R= anked Measures Expected search length Average rank of the first relevant document Mean precision at a fixed number of documents Precision at 10 docs is often used for Web search Mean precision at a fixed recall level Adjusts for the total number of relevant docs Mean breakeven point Value at which precision =3D recall
19	Automatic Evalu= ation Model
20	Ad Hoc Topics In TREC, a statement of information need is called a topic
21	Questions About= the Black Box Example “questions”: Does morphological analysis improve retrieval performance? Does expanding the query with synonyms improve retrieval performanc= e? Corresponding experiments: Build a “stemmed” index and compare against “unstemmed” baseline Expand queries with synonyms and compare against baseline unexpanded queries
22	Measuring Preci= sion and Recall
23	Graphing Precis= ion and Recall Plot each (recall, precision) point on a graph Visually represent the precision/recall tradeoff
24	Uninterpolated = Average Precision Average of precision at each retrieved relevant document Relevant documents not retrieved contribute zero to score
25	Uninterpolated = MAP
26	Visualizing Mean Average Precision
27	What MAP Hides<= /a>
28	Why Mean Average Precision? It is easy to trade between recall and precision Adding related query terms improves recall But naive query expansion techniques kill precision Limiting matches by part-of-speech helps precision But it almost always hurts recall Comparisons should give some weight to both Average precision is a principled way to do this More “central” than other available measures
29	Systems Ranked = by Different Measures
30	Correlations Be= tween Rankings
31	Relevant Docume= nt Density
32	Alternative Way= s to Get Judgments Exhaustive assessment is usually impractical Topics * documents =3D a large number! Pooled assessment leverages cooperative evaluation Requires a diverse set of IR systems Search-guided assessment is sometimes viable Iterate between topic research/search/assessment Augment with review, adjudication, reassessment Known-item judgments have the lowest cost Tailor queries to retrieve a single known document Useful as a first cut to see if a new technique is viable
33	Obtaining Relev= ance Judgments Exhaustive assessment can be too expensive TREC has 50 queries for >1 million docs each year Random sampling won’t work If relevant docs are rare, none may be found! IR systems can help focus the sample Each system finds some relevant documents Different systems find different relevant documents Together, enough systems will find most of them
34	Pooled Assessme= nt Methodology Systems submit top 1000 documents per topic Top 100 documents for each are judged Single pool, without duplicates, arbitrary order Judged by the person that wrote the query Treat unevaluated documents as not relevant Compute MAP down to 1000 documents Treat precision for complete misses as 0.0
35	Does pooling wo= rk? Judgments can’t possibly be exhaustive! This is only one person’s opinion about relevance What about hits 101 to 1000? We can’t possibly use judgments to evaluate a system that didn’t participate in the evaluation!
36	Lessons From TR= EC Incomplete judgments are useful If sample is unbiased with respect to systems tested Different relevance judgments change absolute score But rarely change comparative advantages when averaged Evaluation technology is predictive Results transfer to operational settings
37	Effects of Inco= mplete Judgments Additional relevant documents are: roughly uniform across systems highly skewed across topics Systems that don’t contribute to pool get comparable results
38	Inter-Judge Agr= eement
39	Effect of Diffe= rent Judgments
40	Net Effect of D= ifferent Judges Mean Kendall t between system rankings produced from different qrel sets: .938 Similar results held for Different query sets Different evaluation measures Different assessor types Single opinion vs. group opinion judgments
41	Statistical Significance Tests How sure can you be that an observed difference doesn’t simply result from the particular queries you chose?
42	Statistical Significance Testing
43	How Much is Eno= ugh? Measuring improvement Achieve a meaningful improvement Guideline: 0.05 is noticeable, 0.1 makes a difference Achieve reliable improvement on “typical” queries Wilcoxon signed rank test for paired samples Know when to stop! Inter-assessor agreement limits max precision Using one judge to assess the other yields about 0.8
44	Recap: Automatic Evaluation Evaluation measures focus on relevance Users also want utility and understandability Goal is to compare systems Values may vary, but relative differences are stable Mean values obscure important phenomena Augment with failure analysis/significance tests
45	Automatic Evalu= ation
46	User Studies
47	User Studies Goal is to account for interface issues By studying the interface component By studying the complete system Formative evaluation Provide a basis for system development Summative evaluation Designed to assess performance
48	Questions That = Involve Users Example “questions”: Does keyword highlighting help users evaluate document relevance? Is letting users weight search terms a good idea? Corresponding experiments: Build two different interfaces, one with keyword highlighting, one without; run a user study Build two different interfaces, one with term weighting functionali= ty, and one without; run a user study
49	Blair and Maron= (1985) A classic study of retrieval effectiveness Earlier studies used unrealistically small collections Studied an archive of documents for a lawsuit 40,000 documents, ~350,000 pages of text 40 different queries Used IBM’s STAIRS full-text system Approach: Lawyers wanted at least 75% of all relevant documents Precision and recall evaluated only after the lawyers were satisfied with the results
50	Blair and Maron= ’s Results Mean precision: 79% Mean recall: 20% (!!) Why recall was low? Users can’t anticipate terms used in relevant documents Differing technical terminology Slang, misspellings Other findings: Searches by both lawyers had similar performance Lawyer’s recall was not much different from paralegal’s=
51	Quantitative Us= er Studies Select independent variable(s) e.g., what info to display in selection interface Select dependent variable(s) e.g., time to find a known relevant document Run subjects in different orders Average out learning and fatigue effects Compute statistical significance Null hypothesis: independent variable has no effect Rejected if p<0.05
52	Variation in Au= tomatic Measures System What we seek to measure Topic Sample topic space, compute expected value Topic+System Pair by topic and compute statistical significance Collection Repeat the experiment using several collections
53	Additional Effe= cts in User Studies Learning Vary topic presentation order Fatigue Vary system presentation order Topic+User (Expertise) Ask about prior knowledge of each topic
54	Presentation Or= der
55	Batch vs. User Evaluations Do batch (black box) and user evaluations give the same results? If = not, why? Two different tasks: Instance recall (6 topics) Question answering (8 topics)
56	Results Compared of two systems: a baseline system an improved system that was provably better in batch evaluations Results:
57	Example User St= udy Results
58	Qualitative User Studies Observe user behavior Instrumented software, eye trackers, etc. Face and keyboard cameras Think-aloud protocols Interviews and focus groups Organize the data For example, group it into overlapping categories Look for patterns and themes Develop a “grounded theory”
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60	Topicality<= /font>
61	Questionnaires<= /a> Demographic data For example, computer experience Basis for interpreting results Subjective self-assessment Which did they think was more effective? Often at variance with objective results! Preference Which interface did they prefer?&n= bsp; Why?
62	Summary Qualitative user studies suggest what to build Design decomposes task into components Automated evaluation helps to refine components Quantitative user studies show how well it works
63	One Minute Pape= r If I demonstrated a new retrieval technique that achieved a statistically significant improvement in average precision on the TR= EC collection, what would be the most serious limitation to consider wh= en interpreting that result?