Validity and Reliability of Cranfield-like Evaluation in Information Retrieval

Validity and Reliability of
Cranfield-like Evaluation
in Information Retrieval
Julián Urbano
Picture by Tom Parnell Glasgow, Scotland · September 2013

Talk outline
• Why we want to Evaluate…
• …and what we do with Cranfield
• Validity: users versus systems
• Reliablity: estimating from samples

The two questions
• How good is my system?
– What does good mean?
– What is good enough?
• Is system A better than system B?
– What does better mean?
– How much better?
• Efficiency? Effectiveness? Ease?

Measure user experience
• Time to complete task
• Idle time
• Success rate
• Failure rate
• Frustration
• Ease to learn
• Ease to use
…and a long etcetera

We want to know some distributions
• For an arbitrary user, need and document
collection, what is the distribution of:
• They describe user experience, fully
0
time to complete task
none
frustration
muchsome

The big(ger) picture
• Different user-measures attempting to assess the
same thing: user satisfaction
– How likely is it that an arbitrary user, with an arbitrary
need (and with an arbitrary document collection) will
be satisfied by the system?
• This is the ultimate goal: the good, the better

The big(ger) question
• User satisfaction…as Bernoulli trial
• Probability of satisfaction?
• Probability that k in n users are satisfied?
• Probability of >80% users satisfied?
satisfaction
yesno

Sources of variability
user-measure = f(documents, need, user, system)
• Try to estimate the user-measure distribution
– Sample documents, needs and users
– Problematic
• Representativeness
• Cost
• Ethics
– Hard to replicate and repeat results

Fix samples
• Get a (hopefully) good sample and fix it
– Document collection
– Topic set
– A step towards reproducibility
• Still have to sample users, but can’t fix them!
– Very large source of variability
– Hard to replicate and repeat experiments
– Complex, costly, ethical issues
– Example: ASTIA-Uniterm studies

Simulate users…and fix them
• Cleverdon’s idea: remove users, but include a
static user component, fixed across experiments
– The judgments in the ground truth
• Remove all sources of variability, except systems

Simulate users…and fix them
• Cleverdon’s idea: remove users, but include a
static user component, fixed across experiments
– The judgments in the ground truth
• Remove all sources of variability, except systems
user-measure = f(system)

Test collections
user-measure = f(system)
• Test collections are tools to estimate
distributions of user-measures
– Reproducibility becomes possible and easy
– Experiments are inexpensive (collections are not)
– Research becomes systematic

Wait a minute
• Are we estimating distributions about users or
distributions about systems?
system-effectiveness = f(system, measure)
• We come up with different distributions of
system-effectiveness, one per measure
• Each measure has its own assumptions

Assumption
• System-measures correspond to user-measures
Users Systems
Time to complete task
Idle time
Success rate
Failure rate
Frustration
Ease to learn
Ease to use
Satisfaction
…
P
AP
RR
DCG
nDCG
ERR
GAP
Q
…

Assumption
• Well, at least we assume the correlation
– Are they correlated? How well?
• Test collections: estimators of user distributions
– What we want to measure: user satisfaction
– What we do measure: system effectiveness

Validity and Reliability
• Validity: are we measuring what we want to?
– External validity:
Are topics, documents and assessors representative?
– Construct validity:
Do system-measures correspond to user-measures?
– Conclusion validity:
Is system A really better than system B?
• Reliability: how repeatable are the results?
– How large do collections have to be to ensure
repeatability with a different sample?

Assumption
• Systems with better effectiveness are perceived
by users as more useful, more satisfactory
• Tricky: different effectiveness measures and
relevance scales give different results
– Which one is better to predict satisfaction?
• The goal is user satisfaction, not system
effectiveness

Mapping
• Try to map system effectiveness onto user
satisfaction, experimentally
• If P@10 = 0.2, how likely is it that the user will
find the results satisfactory?
• What if DCG@20 = 0.467?
• What if ERR = 0.9?

User-oriented System-measures
• Effectiveness measures are generally not
formulated to correlate with user-satisfaction
• If effectiveness is 0, we expect 0% probability of
user satisfaction
• If effectiveness is 1, we expect 100% probability
• If effectiveness is 𝜆, we expect 100𝜆%
• But this is not what we have

Unbounded measures
𝐷𝐶𝐺@𝑘 =
𝑔𝑎𝑖𝑛 𝑟𝑖
𝑑𝑖𝑠𝑐𝑜𝑢𝑛𝑡 𝑖
𝑘
𝑖=1
• Upper bound depends on cutoff, gain function
and relevance scale
– Normalize effectiveness between 0 and 1
– What is the best we can do with 𝑘 documents?
𝐷𝐶𝐺@𝑘 =
𝑔𝑎𝑖𝑛 𝑟𝑖 𝑑𝑖𝑠𝑐𝑜𝑢𝑛𝑡 𝑖
∗
𝑑𝑖𝑠𝑐𝑜𝑢𝑛𝑡 𝑖
𝑘
𝑖=1

Recall-oriented measures
𝐴𝑃@𝑘 =
1
ℛ1
𝑟i · 𝑃@𝑖
𝑘
𝑖=1
• 𝐴𝑃@𝑘 = 1 only possible if 𝑘 ≥ ℛ1
• Reformulate towards users
– What is the best we can do with 𝑘 documents,
regardless of the judgments in the ground truth?
𝐴𝑃@𝑘 =
1
𝑘
𝑟𝐴 𝑖
· 𝑃@𝑖
𝑘
𝑖=1

Ideal ranking
𝑛𝐷𝐶𝐺@𝑘 =
𝑔𝑎𝑖𝑛 𝑟𝑖 𝑑𝑖𝑠𝑐𝑜𝑢𝑛𝑡 𝑖𝑘
𝑖=1
𝑔𝑎𝑖𝑛 𝑖𝑑𝑒𝑎𝑙𝑖 𝑑𝑖𝑠𝑐𝑜𝑢𝑛𝑡 𝑖𝑘
𝑖=1
• If there is only one relevant, 𝑛𝐷𝐶𝐺@10 = 1
even if we retrieve nine nonrelevants
• Assume the ideal ranking has only excellent
documents, with maximum relevance
𝑛𝐷𝐶𝐺@𝑘 =
𝑔𝑎𝑖𝑛 𝑟𝑖 𝑑𝑖𝑠𝑐𝑜𝑢𝑛𝑡 𝑖𝑘
𝑖=1
∗
𝑑𝑖𝑠𝑐𝑜𝑢𝑛𝑡 𝑖𝑘
𝑖=1
• This is basically user-oriented 𝐷𝐶𝐺@𝑘

Audio Music Similarity
• Song as input to system, audio signal
• Retrieve songs musically similar to it, by content
• Resembles traditional Ad Hoc retrieval in Text IR
• (most?) Important task in Music IR
– Music recommendation
– Playlist generation
– Plagiarism detection

Measures
• All reformulated, user-oriented
– What is the best we can do under the user model?
• Binary
– P, AP, RR
• Graded
– CG, DCG, Q, RBP, ERR, GAP, ADR , EDCG
– Linear and exponential gains

Relevance scales
• Originally used
– Broad: 3 levels
– Fine: 101 levels
• Artificially made from the Fine scale
– Graded with 3, 4 and 5 levels, evenly spaced
– Binary, with threshold equal 20, 40, 60 and 80

Measures and Scales
Measure
Original Artificial Graded Artificial Binary
Broad Fine 𝑛ℒ = 3 𝑛ℒ = 4 𝑛ℒ = 5 ℓ 𝑚𝑖𝑛 = 20 ℓ 𝑚𝑖𝑛 = 40 ℓ 𝑚𝑖𝑛 = 60 ℓ 𝑚𝑖𝑛 = 80
𝑃@5 x x x x
𝐴𝑃@5 x x x x
𝑅𝑅@5 x x x x
𝐶𝐺𝑙@5 x x x x x 𝑃@5 𝑃@5 𝑃@5 𝑃@5
𝐶𝐺𝑒@5 x x x x 𝑃@5 𝑃@5 𝑃@5 𝑃@5
𝐷𝐶𝐺𝑙@5 x x x x x x x x x
𝐷𝐶𝐺𝑒@5 x x x x 𝐷𝐶𝐺𝑙@5 𝐷𝐶𝐺𝑙@5 𝐷𝐶𝐺𝑙@5 𝐷𝐶𝐺𝑙@5
𝐸𝐷𝐶𝐺𝑙@5 x x x x x x x x x
𝐸𝐷𝐶𝐺𝑒@5 x x x x 𝐸𝐷𝐶𝐺𝑙@5 𝐸𝐷𝐶𝐺𝑙@5 𝐸𝐷𝐶𝐺𝑙@5 𝐸𝐷𝐶𝐺𝑙@5
𝑄𝑙@5 x x x x x 𝐴𝑃@5 𝐴𝑃@5 𝐴𝑃@5 𝐴𝑃@5
𝑄 𝑒@5 x x x x 𝐴𝑃@5 𝐴𝑃@5 𝐴𝑃@5 𝐴𝑃@5
𝑅𝐵𝑃𝑙@5 x x x x x x x x x
𝑅𝐵𝑃𝑒@5 x x x x 𝑅𝐵𝑃𝑙@5 𝑅𝐵𝑃𝑙@5 𝑅𝐵𝑃𝑙@5 𝑅𝐵𝑃𝑙@5
𝐸𝑅𝑅𝑙@5 x x x x x x x x x
𝐸𝑅𝑅 𝑒@5 x x x x 𝐸𝑅𝑅𝑙@5 𝐸𝑅𝑅𝑙@5 𝐸𝑅𝑅𝑙@5 𝐸𝑅𝑅𝑙@5
𝐺𝐴𝑃@5 x x x x x 𝐴𝑃@5 𝐴𝑃@5 𝐴𝑃@5 𝐴𝑃@5
𝐴𝐷𝑅@5 x x x x x x x x

Experimental Design
user preference
(agrees or disagrees
with effectiveness)

Experimental Design
non-preference
(can’t decide)

What can we infer?
• Preference: difference noticed by user
– Positive: user agrees with evaluation
– Negative: user disagrees with evaluation
• Non-preference: difference not noticed by user
– Good: both systems are satisfactory
– Bad: both systems are not satisfactory

Data
• Queries, documents and judgments from MIREX
– MIREX: TREC-like evaluation forum in Music IR
• 4,115 unique and artificial examples
– Covering full range of effectiveness
• In 10 bins 0, 0.1 , 0.1, 0.2 , … , [0.9, 1]
– At least 200 examples per measure/scale/bin
• 432 unique queries, 5,636 unique documents

Collecting User Preferences
• Crowdsourcing
– Quality control through trap examples
• Total: 547 unique subjects, 11,042 preferences
• Accepted: 175 subjects, 9,373 preferences
• After trap questions: 113 subjects

Single system: how good is it?
• 2,045 non-preferences (49%)
– 1,056 satisfactory
– 969 non-satisfactory
What do we expect?

• 2,045 non-preferences (49%)
– 1,056 satisfactory
– 969 non-satisfactory
Linear
mapping

Large
thresholds
underestimate
satisfaction

Ranking does
not affect
satisfaction?

Exponential
gain
underestimates
satisfaction

• Best adhere to the diagonal
– 𝐶𝐺𝑙@5, 𝐷𝐶𝐺𝑙@5 and 𝑅𝐵𝑃𝑙@5
– Not necessarily better: just easier to interpret
• About 20% bias at endpoints
– Room for improvement with personalization
• Less sensitive to subjectivity in relevance
– Minimize 𝑃(𝑆𝑎𝑡│0) and maximize 𝑃(𝑆𝑎𝑡│1)
– ℓ 𝑚𝑖𝑛 = 40 and 𝐵𝑟𝑜𝑎𝑑 behave better
– 𝐶𝐺@5, 𝐷𝐶𝐺@5, 𝑅𝐵𝑃@5 and 𝐺𝐴𝑃@5

Two systems: which one is better?
• 2,090 preferences (51%)
– 1,019 for system A
– 1,071 for system B
What do we expect?

• 2,090 preferences (51%)
– 1,019 for system A
– 1,071 for system B
Users always
notice the
difference…
…regardless
of how
large it is

Need quite
large
differences!

More relevance
levels better to
discriminate

Bad
correlation?

• Users prefer the (supposedly) worse system

User Agrees with Evaluation
• Closer to ideal 𝑃 𝐴𝑔𝑔 = 1 Δ𝜆 = 1
– ℓ 𝑚𝑖𝑛 = 80 better among binaries
– 𝐹𝑖𝑛𝑒 better for linear gain
– 𝑛ℒ = 5 better for exponential gain
– 𝐶𝐺@5, 𝐷𝐶𝐺@5, 𝑅𝐵𝑃@5 and 𝐺𝐴𝑃@5

User Disagrees with Evaluation
• Closer to ideal 𝑃 𝐴𝑔𝑔 = −1 Δ𝜆 = 0
– ℓ 𝑚𝑖𝑛 = 40 better among binaries
– 𝐹𝑖𝑛𝑒 better for linear gain
– 𝐵𝑟𝑜𝑎𝑑 better with exponential gain
– 𝐶𝐺@5, 𝐺𝐴𝑃@5, 𝐷𝐶𝐺@5 and 𝑅𝐵𝑃@5

Summary
• Linear gain better than exponential gain
– Except, slightly, in terms of disagreements
• Measures oriented to a single document are not
appropriate for a music recommendation setting
• Gain is independent of other documents
• 𝐵𝑟𝑜𝑎𝑑 better to predict satisfaction
• 𝐹𝑖𝑛𝑒 better to predict user agreement
• Binary scales worst overall

Summary
• We can map system effectiveness onto
probability of user satisfaction
• ~20% of users disagree with effectiveness
– Practical upper (and lower) bound in evaluation
– Need to incorporate user profiles
• Somehow included in MSD Challenge
• Δ𝜆 ≈ 0.4 needed for users to agree
– Historically observed only 20% of times in MIREX
– Be careful with statistical significance!

User Satisfaction
• So far only for a query and a user (Bernoulli)
– 𝑃 𝑆𝑎𝑡 𝜆 𝑞
• Easily for 𝑛 users (Binomial)
– 𝑃 𝑆𝑎𝑡 𝑛 = 𝑘 𝜆 𝑞
• Example: 𝑄𝑙@5 = 0.61
– 𝑃 𝑆𝑎𝑡 ≈ 0.7
– 𝑃 𝑆𝑎𝑡15 = 10 ≈ 0.21
• What about a sample of queries 𝒬?

User Satisfaction over a Sample
𝐸 𝑃 𝑆𝑎𝑡 =
1
𝑛 𝒬
𝑃 𝑆𝑎𝑡 𝜆 𝑞
𝑞∈𝒬
• Example: satisfaction is underestimated

System Success
• If 𝑃 𝑆𝑎𝑡 ≥ 𝑡𝑕𝑟𝑒𝑠𝑕𝑜𝑙𝑑 the system is successful
• If we want the majority of users to be satisfied
– 𝑃 𝑆𝑢𝑐𝑐 = 1 − F 𝑃 𝑆𝑎𝑡 0.5
• Intuition: improving bad queries is worthier than
further improving good ones

System Success
• Example:
– 𝐸 Δ𝜆 = −0.0021

System Success
• Example:
– 𝐸 Δ𝜆 = −0.0021
– 𝐸 𝛥𝑃 𝑆𝑎𝑡 = 0.0011

System Success
• Example:
– 𝐸 Δ𝜆 = −0.0021
– 𝐸 𝛥𝑃 𝑆𝑎𝑡 = 0.0011
– 𝐸 Δ𝑃 𝑆𝑢𝑐𝑐 = 0.07

Summary
• Need to consider full distributions
– Always average or good on average?
• Modeling full distribution
– Normal for small query sets, Empirical for large
– Beta always better for 𝐹𝑖𝑛𝑒 scale

Summary
• Intuitive interpretations of effectiveness fail
– Contradictory results in terms of user satisfaction

Samples
• Test collections are samples from larger, possibly
infinite, populations
– Documents, queries and users
• Δ𝜆 is just an estimate of the population mean 𝜇Δ𝜆
• How reliable is our conclusion?

Reliability vs Cost
• Building reliable collections is easy
• Just use more documents, queries and assessors
• But it is prohibitively expensive
• Best option is to increase query set size
– Largest source of variability
• How many queries?
– First we need to measure reliability

Data-based approach
1. Randomly split query set
2. Compute indicators of reliability based on
these two query subsets
3. Extrapolate to larger query sets
…with some variations

Data-based reliability indicators
• Compare results with two collections
– Kendall tau correlation
– AP correlation
– Absolute sensitivity
– Relative sensitivity
– Power ratio
– Minor conflict ratio
– Major conflict ratio
– RMSE

Generalizability Theory approach
• Address variability of scores, not just means
• G-study
– Estimate variance components from previous,
representative data
– Usually previous test collections
• D-study
– Estimate reliability based on estimated variance
components from G-study

G-study
𝜎2 = 𝜎𝑠
2 + 𝜎 𝑞
2 + 𝜎𝑠:𝑞
2
• Estimated with Analysis of Variance

G-study
𝜎2 = 𝜎𝑠
2 + 𝜎 𝑞
2 + 𝜎𝑠:𝑞
2
system
differences,
our goal!

G-study
𝜎2 = 𝜎𝑠
2 + 𝜎 𝑞
2 + 𝜎𝑠:𝑞
2
system
differences,
our goal! query
difficulty

G-study
𝜎2 = 𝜎𝑠
2 + 𝜎 𝑞
2 + 𝜎𝑠:𝑞
2
system
differences,
our goal! query
difficulty
some systems
better for
some queries

D-study
• Relative stability: 𝐸𝜌2
=
𝜎𝑠
2
𝜎𝑠
2+
𝜎 𝑠:𝑞
2
𝑛 𝑞
′
• Absolute stability: Φ =
𝜎𝑠
2
𝜎𝑠
2+
𝜎 𝑞
2+𝜎 𝑠:𝑞
2
𝑛 𝑞
′
• Easy to estimate how many queries we need to
reach a certain stability level (1MQ track)
– ≈80 queries sufficient for stable rankings
– ≈130 queries for stable absolute scores

G-Theory approach
• How sensitive is the D-study to the initial data
used in the G-study?
• How should we interpret G-Theory indicators in
practice? What does 𝐸𝜌2
= 0.95 mean?
• From the above, review reliability of over 40
TREC test collections

Data
• 43 TREC collections
– From TREC 3 to TREC 2011
• 12 tasks across 10 tracks
– Ad hoc, Web, Novelty, Genomics, Robust, Terabyte,
Enterprise, Million Query, Medical and Microblog

Sensitivity: experiment
• Vary number of queries in G-study
– From 𝑛 𝑞 = 5 to full set
– Use all runs available
• Run D-study
– Compute 𝐸𝜌2 and Φ
– Compute 𝑛 𝑞
′ to reach 0.95 stability
• 200 random trials

We may get 𝐸𝜌2 = 0.9 or
𝐸𝜌2 = 0.3, depending on
what 10 queries we use
Variability due to queries

Sensitivity: experiment
• Do the same, but vary number of systems
– From 𝑛 𝑠 = 5 to full set
– Use all queries available

We may get 𝐸𝜌2 = 0.9 or
𝐸𝜌2 = 0.5, depending on
what 20 systems we use
Variability due to systems

Results
• G-Theory is very sensitive to initial data
– Need about 50 queries and 50 systems for differences
in 𝐸𝜌2
and Φ below 0.1
• Number of queries for 𝐸𝜌2
= 0.95 may change
in orders of magnitude
– Microblog2011 (all 184 systems and 30 queries)
• Need 63 to 133 queries
– Medical2011 (all 34 queries and 40 systems)
• Need 109 to 566 queries

Account for variability
in initial data
Compute confidence intervals

Required number of
queries to reach the
lower end of the interval
Compute confidence intervals

Summary in TREC
• 𝐸𝜌2
: mean=0.88 sd=0.1
– 95% conf. intervals are 0.1 long
• Φ: mean=0.74 sd=0.2
– 95% conf. intervals are 0.19 long

Interpretation: experiment
• Split query set in 2 subsets
– From 𝑛 𝑞 = 10 to full set / 2
– Use all runs available
• Run D-study
– Compute 𝐸𝜌2
and Φ and map onto 𝜏, sensitivity,
power, conflicts, etc.
– Over 28,000 datapoints

*All mappings in the paper
Example: 𝑬𝝆 𝟐 → 𝝉

𝐸𝜌2 = 0.95 → 𝜏 ≈ 0.85

𝜏 = 0.9 → 𝐸𝜌2
≈ 0.97

Million
Query
2007
Million Query 2008

Future predictions
• This allows us to make more informed decisions
within a collection
• What about a new collection?
– Fit a single model for each mapping with 90% and
95% prediction intervals
• Assess whether a larger collection is really worth
the effort

current collection

current collection target

Example: 𝚽 → 𝒓𝒆𝒍. 𝒔𝒆𝒏𝒔𝒊𝒕𝒗𝒊𝒕𝒚

Summary
• G-Theory is regarded as more appropriate, ease
to use and powerful to assess reliability than the
traditional data-based approaches
• But it is quite sensitive to initial data used to
estimate variance components
– Data-based approaches are too!
• and almost impossible to interpret in practice

Summary
• Need about 50 queries and 50 systems to have
robust estimates of reliability
– That is a whole collection already!
– Need to use confidence intervals
• Previous interpretation overestimated reliability
– 𝜏 = 0.9 → 𝐸𝜌2 ≈ 0.97
– 𝐸𝜌2
= 0.95 → 𝜏 ≈ 0.85

Reliability:reviewofTRECcollections

Outline
• Estimate 𝐸𝜌2
and Φ, with 95% confidence
intervals, and full query set
• Map onto 𝜏, sensitivity, power, conflicts, etc.
• Results within tasks offer a historical perspective
on reliability since 1994

*All collections and mappings in the paper
Example: Ad hoc 3-8
• 𝐸𝜌2
∈ 0.86,0.93 → 𝜏 ∈ [0.65,0.81]
• 𝑚𝑖𝑛𝑜𝑟 𝑐𝑜𝑛𝑓𝑙𝑖𝑐𝑡𝑠 ∈ 0.6, 8.2 %
• 𝑚𝑎𝑗𝑜𝑟 𝑐𝑜𝑛𝑓𝑙𝑖𝑐𝑡𝑠 ∈ 0.02, 1.38 %
• Queries to get 𝐸𝜌2
= 0.95: [37,233]
• Queries to get Φ = 0.95: [116,999]
• 50 queries were used

Example: Web ad hoc
• TREC-8 to TREC-2001: WT2g and WT10g
– 𝐸𝜌2 ∈ 0.86,0.93 → 𝜏 ∈ [0.65,0.81]
– Queries to get 𝐸𝜌2 = 0.95: 40,220
• TREC-2009 to TREC-2011: ClueWeb09
– 𝐸𝜌2 ∈ 0.8,0.83 → 𝜏 ∈ [0.53,0.59]
– Queries to get 𝐸𝜌2
= 0.95: 107,438
• 50 queries were used

Historical trend
• Decreasing within and across tracks?

Historical trend
• Systems getting better for specific problems?

Historical trend
• Increasing task-specificity in queries?

Historical reliability in TREC
• On average, 𝐸𝜌2
= 0.88 → 𝜏 ≈ 0.7
• Some collections clearly unreliable
– Web Distillation 2003, Genomics 2005, Terabyte 2006,
Enterprise 2008, Medical 2011 and Web Ad Hoc 2011
• 50 queries not enough for stable rankings, about
200 are needed in most cases

Implications
• Fixing a minimum number of queries across
tracks is unrealistic
– Not even across editions of the same task
• Need to analyze on a case-by-case basis, while
building the collections
– GT4IReval, R package online

Validity
• Similar studies in Text IR to map effectiveness
onto user satisfaction
• Particularly interesting because there are several
query types, and users behave differently
– Single measure to use in all cases?
– Use different measures and average them all?
• Further user studies to figure out what makes
users say good and better
• How should test collections be extended to
incorporate more user information?

Reliability
• Study assessor effect
• Study document collection effect
• Better models to map G-theory indicators onto
understandable data-based indicators
• Methods to reliably measure reliability while
building the collection

General
• Cleverdon, C. W. (1991). The Significance of the Cranfield Tests on Index Languages. In International ACM SIGIR
Conference on Research and Development in Information Retrieval (pp. 3–12).
• Sanderson, M. (2010). Test Collection Based Evaluation of Information Retrieval Systems. Foundations and Trends
in Information Retrieval, 4(4), 247–375.
• Robertson, S. (2008). On the History of Evaluation in IR. Journal of Information Science, 34(4), 439–456.
• Harman, D. K. (2011). Information Retrieval Evaluation. Synthesis Lectures on Information Concepts, Retrieval,
and Services, 3(2), 1–119.
• Voorhees, E. M. (2002). The Philosophy of Information Retrieval Evaluation. In Workshop of the Cross-Language
Evaluation Forum (pp. 355–370).
• Tague-Sutcliffe, J. (1992). The Pragmatics of Information Retrieval Experimentation, Revisited. Information
Processing and Management, 28(4), 467–490.
• Gull, C. D. (1956). Seven Years of Work on the Organisation of Materials in a Special Library. American
Documentation, 7(4), 320–329.
• Urbano, J., Schedl, M., & Serra, X. (2013). Evaluation in Music Information Retrieval. Journal of Intelligent
Information Systems.
• Urbano, J. (2013). Evaluation in Audio Music Similarity. PhD dissertation, University Carlos III of Madrid.
• Trochim, W. M. K., & Donnelly, J. P. (2007). The Research Methods Knowledge Base (3rd ed.). Atomic Dog
Publishing.
• Shadish, W. R., Cook, T. D., & Campbell, D. T. (2002). Experimental and Quasi-Experimental Designs for
Generalized Causal Inference. Houghton-Mifflin.
• Zobel, J., Webber, W., Sanderson, M., & Moffat, A. (2011). Principles for Robust Evaluation Infrastructure. In ACM
CIKM Workshop on Data infrastructures for Supporting Information Retrieval Evaluation.

Validity
• Allan, J., Carterette, B., & Lewis, J. (2005). When Will Information Retrieval Be “Good Enough”? In International
ACM SIGIR Conference on Research and Development in Information Retrieval (pp. 433–440).
• Al-Maskari, A., Sanderson, M., & Clough, P. (2007). The Relationship between IR Effectiveness Measures and User
Satisfaction. In International ACM SIGIR Conference on Research and Development in Information Retrieval (pp.
773–774).
• Al-Maskari, A., Sanderson, M., Clough, P., & Airio, E. (2008). The Good and the Bad System: Does the Test
Collection Predict User’s Effectiveness. In International ACM SIGIR Conference on Research and Development in
Information Retrieval (pp. 59–66).
• Bailey, P., Craswell, N., Soboroff, I., Thomas, P., Vries, A. P. de, & Yilmaz, E. (2008). Relevance Assessment: Are
Judges Exchangeable and Does it Matter? In International ACM SIGIR Conference on Research and Development
in Information Retrieval (pp. 667–674).
• Bennett, P. N., Carterette, B., Chapelle, O., & Joachims, T. (2008). Beyond Binary Relevance: Preferences, Diversity
and Set-Level Judgments. ACM SIGIR Forum, 42(2), 53–58.
• Carterette, B. (2011). System Effectiveness, User Models, and User Utility: A General Framework for
Investigation. In International ACM SIGIR Conference on Research and Development in Information Retrieval (pp.
903–912).
• Carterette, B., Bennett, P. N., Chickering, D. M., & Dumais, S. T. (2008). Here or There: Preference Judgments for
Relevance. In European Conference on Information Retrieval (pp. 16–27).
• Carterette, B., & Soboroff, I. (2010). The Effect of Assessor Error on IR System Evaluation. In International ACM
SIGIR Conference on Research and Development in Information Retrieval (pp. 539–546).
• Hersh, W., Turpin, A., Price, S., Chan, B., Kraemer, D., Sacherek, L., & Olson, D. (2000). Do Batch and User
Evaluations Give the Same Results? In International ACM SIGIR Conference on Research and Development in

Validity
• Hersh, W., Turpin, A., Sacherek, L., Olson, D., Price, S., Chan, B., & Kraemer, D. (2000). Further Analysis of
Whether Batch and User Evaluations Give the Same Results With a Question-Answering Task. In Text REtrieval
Conference.
• Hu, X., & Kando, N. (2012). User-Centered Measures vs. System Effectiveness in Finding Similar Songs. In
International Society for Music Information Retrieval Conference (pp. 331–336).
• Huffman, S. B., & Hochster, M. (2007). How Well does Result Relevance Predict Session Satisfaction? In
International ACM SIGIR Conference on Research and Development in Information Retrieval (pp. 567–573).
• Ingwersen, P., & Järvelin, K. (2005). The Turn: Integration of Information Seeking and Retrieval in Context.
Springer.
• Järvelin, K. (2011). IR Research: Systems, Interaction, Evaluation and Theories. ACM SIGIR Forum, 45(2), 17–31.
• Mizzaro, S. (1997). Relevance: The Whole History. Journal of the American Society for Information Science, 48(9),
810–832.
• Sanderson, M., Paramita, M. L., Clough, P., & Kanoulas, E. (2010). Do User Preferences and Evaluation Measures
Line Up? In International ACM SIGIR Conference on Research and Development in Information Retrieval (pp. 555–
562).
• Schedl, M., Flexer, A., & Urbano, J. (2013). The Neglected User in Music Information Retrieval Research. Journal
of Intelligent Information Systems.
• Schedl, M., Stober, S., Gómez, E., Orio, N., & Liem, C. C. S. (2012). User-Aware Music Retrieval. In M. Müller, M.
Goto, & M. Schedl (Eds.), Multimodal Music Processing (pp. 135–156). Dagstuhl Publishing.
• Scholer, F., & Turpin, A. (2008). Relevance Thresholds in System Evaluations. In International ACM SIGIR

Validity
• Smucker, M. D., & Clarke, C. L. A. (2012). The Fault, Dear Researchers, is Not in Cranfield, But in Our Metrics, that
They Are Unrealistic. In European Workshop on Human-Computer Interaction and Information Retrieval (pp. 11–
12).
• Thom, J. A., & Scholer, F. (2007). A Comparison of Evaluation Measures Given How Users Perform on Search
Tasks. In Australasian Document Computing Symposium (pp. 100–103).
• Turpin, A., & Hersh, W. (2001). Why Batch and User Evaluations Do Not Give the Same Results. In International
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• Turpin, A., & Scholer, F. (2006). User Performance Versus Precision Measures for Simple Search Tasks. In
• Urbano, J., Downie, J. S., Mcfee, B., & Schedl, M. (2012). How Significant is Statistically Significant? The case of
Audio Music Similarity and Retrieval. In International Society for Music Information Retrieval Conference (pp.
181–186).

Reliability
• Allan, J., Aslam, J. A., Carterette, B., Pavlu, V., & Kanoulas, E. (2008). Million Query Track 2008 Overview. In Text
REtrieval Conference.
• Allan, J., Carterette, B., Aslam, J. A., Pavlu, V., Dachev, B., & Kanoulas, E. (2007). Million Query Track 2007
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• Armstrong, T. G., Moffat, A., Webber, W., & Zobel, J. (2009). Improvements that Don’t Add Up: Ad-Hoc Retrieval
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Management, 44(3), 1117–1145.
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• Brennan, R. L. (2001). Generalizability Theory. Springer.
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• Carterette, B., Pavlu, V., Kanoulas, E., Aslam, J. A., & Allan, J. (2008). Evaluation Over Thousands of Queries. In
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• Lin, W.-H., & Hauptmann, A. (2005). Revisiting the Effect of Topic Set Size on Retrieval Error. In International ACM

Reliability
• Cormack, G. V., & Lynam, T. R. (2006). Statistical Precision of Information Retrieval Evaluation. In International
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632).
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Information Retrieval Evaluation. In International ACM SIGIR Conference on Research and Development in
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Reliability
• Voorhees, E. M., & Buckley, C. (2002). The Effect of Topic Set Size on Retrieval Experiment Error. In International
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