Validity and Reliability of Cranfield-like Evaluation in Information Retrieval

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Validity and Reliability of Cranfield-like Evaluation in Information Retrieval

  1. 1. Validity and Reliability of Cranfield-like Evaluation in Information Retrieval Julián Urbano Picture by Tom Parnell Glasgow, Scotland · September 2013
  2. 2. Talk outline • Why we want to Evaluate… • …and what we do with Cranfield • Validity: users versus systems • Reliablity: estimating from samples
  3. 3. WhywewanttoEvaluate
  4. 4. 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?
  5. 5. Measure user experience • Time to complete task • Idle time • Success rate • Failure rate • Frustration • Ease to learn • Ease to use …and a long etcetera
  6. 6. 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
  7. 7. 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
  8. 8. 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
  9. 9. Whatwedowith Cranfield
  10. 10. 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
  11. 11. 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
  12. 12. 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(documents, need, user, system)
  13. 13. 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(documents, need, user, system) user-measure = f(system)
  14. 14. 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
  15. 15. 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
  16. 16. 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 …
  17. 17. 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 …
  18. 18. 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 …
  19. 19. 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 …
  20. 20. 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 …
  21. 21. 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
  22. 22. 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?
  23. 23. Validity
  24. 24. 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
  25. 25. 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?
  26. 26. 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
  27. 27. 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 𝑔𝑎𝑖𝑛 𝑟𝑖 ∗ 𝑑𝑖𝑠𝑐𝑜𝑢𝑛𝑡 𝑖 𝑘 𝑖=1
  28. 28. 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
  29. 29. 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 𝑔𝑎𝑖𝑛 𝑟𝑖 ∗ 𝑑𝑖𝑠𝑐𝑜𝑢𝑛𝑡 𝑖𝑘 𝑖=1 • This is basically user-oriented 𝐷𝐶𝐺@𝑘
  30. 30. 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
  31. 31. 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
  32. 32. 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
  33. 33. 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
  34. 34. Experimental Design
  35. 35. Experimental Design user preference (agrees or disagrees with effectiveness)
  36. 36. Experimental Design non-preference (can’t decide)
  37. 37. 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
  38. 38. 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
  39. 39. 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
  40. 40. Single system: how good is it? • 2,045 non-preferences (49%) – 1,056 satisfactory – 969 non-satisfactory What do we expect?
  41. 41. Single system: how good is it? • 2,045 non-preferences (49%) – 1,056 satisfactory – 969 non-satisfactory Linear mapping
  42. 42. Single system: how good is it? Large thresholds underestimate satisfaction
  43. 43. Single system: how good is it? Ranking does not affect satisfaction?
  44. 44. Single system: how good is it? Exponential gain underestimates satisfaction
  45. 45. Single system: how good is it? • 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
  46. 46. Two systems: which one is better? • 2,090 preferences (51%) – 1,019 for system A – 1,071 for system B What do we expect?
  47. 47. Two systems: which one is better? • 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
  48. 48. Two systems: which one is better? Need quite large differences!
  49. 49. Two systems: which one is better? More relevance levels better to discriminate
  50. 50. Two systems: which one is better? Bad correlation?
  51. 51. Two systems: which one is better? • Users prefer the (supposedly) worse system
  52. 52. 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
  53. 53. 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
  54. 54. 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
  55. 55. 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!
  56. 56. Satisfactionoversamples
  57. 57. 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?
  58. 58. User Satisfaction over a Sample 𝐸 𝑃 𝑆𝑎𝑡 = 1 𝑛 𝒬 𝑃 𝑆𝑎𝑡 𝜆 𝑞 𝑞∈𝒬 • Example: satisfaction is underestimated
  59. 59. 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
  60. 60. System Success • Example: – 𝐸 Δ𝜆 = −0.0021
  61. 61. System Success • Example: – 𝐸 Δ𝜆 = −0.0021 – 𝐸 𝛥𝑃 𝑆𝑎𝑡 = 0.0011
  62. 62. System Success • Example: – 𝐸 Δ𝜆 = −0.0021 – 𝐸 𝛥𝑃 𝑆𝑎𝑡 = 0.0011 – 𝐸 Δ𝑃 𝑆𝑢𝑐𝑐 = 0.07
  63. 63. 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
  64. 64. Summary • Intuitive interpretations of effectiveness fail – Contradictory results in terms of user satisfaction
  65. 65. Reliability
  66. 66. 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?
  67. 67. 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
  68. 68. 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
  69. 69. 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
  70. 70. 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
  71. 71. G-study 𝜎2 = 𝜎𝑠 2 + 𝜎 𝑞 2 + 𝜎𝑠:𝑞 2 • Estimated with Analysis of Variance
  72. 72. G-study 𝜎2 = 𝜎𝑠 2 + 𝜎 𝑞 2 + 𝜎𝑠:𝑞 2 • Estimated with Analysis of Variance system differences, our goal!
  73. 73. G-study 𝜎2 = 𝜎𝑠 2 + 𝜎 𝑞 2 + 𝜎𝑠:𝑞 2 • Estimated with Analysis of Variance system differences, our goal! query difficulty
  74. 74. G-study 𝜎2 = 𝜎𝑠 2 + 𝜎 𝑞 2 + 𝜎𝑠:𝑞 2 • Estimated with Analysis of Variance system differences, our goal! query difficulty some systems better for some queries
  75. 75. 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
  76. 76. 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
  77. 77. 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
  78. 78. 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
  79. 79. Variability due to queries
  80. 80. We may get 𝐸𝜌2 = 0.9 or 𝐸𝜌2 = 0.3, depending on what 10 queries we use Variability due to queries
  81. 81. Sensitivity: experiment • Do the same, but vary number of systems – From 𝑛 𝑠 = 5 to full set – Use all queries available • 200 random trials
  82. 82. Variability due to systems
  83. 83. We may get 𝐸𝜌2 = 0.9 or 𝐸𝜌2 = 0.5, depending on what 20 systems we use Variability due to systems
  84. 84. 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
  85. 85. Compute confidence intervals
  86. 86. Compute confidence intervals
  87. 87. Account for variability in initial data Compute confidence intervals
  88. 88. Required number of queries to reach the lower end of the interval Compute confidence intervals
  89. 89. 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
  90. 90. 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. • 50 random trials – Over 28,000 datapoints
  91. 91. *All mappings in the paper Example: 𝑬𝝆 𝟐 → 𝝉
  92. 92. 𝐸𝜌2 = 0.95 → 𝜏 ≈ 0.85 *All mappings in the paper Example: 𝑬𝝆 𝟐 → 𝝉
  93. 93. 𝜏 = 0.9 → 𝐸𝜌2 ≈ 0.97 *All mappings in the paper Example: 𝑬𝝆 𝟐 → 𝝉
  94. 94. Million Query 2007 Million Query 2008 *All mappings in the paper Example: 𝑬𝝆 𝟐 → 𝝉
  95. 95. 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
  96. 96. *All mappings in the paper Example: 𝑬𝝆 𝟐 → 𝝉
  97. 97. current collection *All mappings in the paper Example: 𝑬𝝆 𝟐 → 𝝉
  98. 98. current collection target *All mappings in the paper Example: 𝑬𝝆 𝟐 → 𝝉
  99. 99. Example: 𝚽 → 𝒓𝒆𝒍. 𝒔𝒆𝒏𝒔𝒊𝒕𝒗𝒊𝒕𝒚
  100. 100. Example: 𝚽 → 𝒓𝒆𝒍. 𝒔𝒆𝒏𝒔𝒊𝒕𝒗𝒊𝒕𝒚
  101. 101. 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
  102. 102. 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
  103. 103. Reliability:reviewofTRECcollections
  104. 104. 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
  105. 105. *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
  106. 106. 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
  107. 107. Historical trend • Decreasing within and across tracks?
  108. 108. Historical trend • Systems getting better for specific problems?
  109. 109. Historical trend • Increasing task-specificity in queries?
  110. 110. 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
  111. 111. 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
  112. 112. Currentand future work
  113. 113. 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?
  114. 114. 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
  115. 115. References
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