Evaluation in Audio Music Similarity

  • 520 views
Uploaded on

Audio Music Similarity is a task within Music Information Retrieval that deals with systems that retrieve songs musically similar to a query song according to their audio content. Evaluation …

Audio Music Similarity is a task within Music Information Retrieval that deals with systems that retrieve songs musically similar to a query song according to their audio content. Evaluation experiments are the main scientific tool in Information Retrieval to determine what systems work better and advance the state of the art accordingly. It is therefore essential that the conclusions drawn from these experiments are both valid and reliable, and that we can reach them at a low cost. This dissertation studies these three aspects of evaluation experiments for the particular case of Audio Music Similarity, with the general goal of improving how these systems are evaluated. The traditional paradigm for Information Retrieval evaluation based on test collections is approached as an statistical estimator of certain probability distributions that characterize how users employ systems. In terms of validity, we study how well the measured system distributions correspond to the target user distributions, and how this correspondence affects the conclusions we draw from an experiment. In terms of reliability, we study the optimal characteristics of test collections and statistical procedures, and in terms of efficiency we study models and methods to greatly reduce the cost of running an evaluation experiment.

More in: Technology , Business
  • Full Name Full Name Comment goes here.
    Are you sure you want to
    Your message goes here
    Be the first to comment
No Downloads

Views

Total Views
520
On Slideshare
0
From Embeds
0
Number of Embeds
1

Actions

Shares
Downloads
0
Comments
0
Likes
1

Embeds 0

No embeds

Report content

Flagged as inappropriate Flag as inappropriate
Flag as inappropriate

Select your reason for flagging this presentation as inappropriate.

Cancel
    No notes for slide

Transcript

  • 1. Evaluation in Audio Music Similarity PhD dissertation by Julián Urbano Picture by Javier García Leganés, October 3rd 2013
  • 2. Outline • • • • • Introduction Validity Reliability Efficiency Conclusions and Future Work 2
  • 3. Outline • Introduction – Scope – The Cranfield Paradigm • • • • Validity Reliability Efficiency Conclusions and Future Work 3
  • 4. Information Retrieval • Automatic representation, storage and search of unstructured information – Traditionally textual information – Lately multimedia too: images, video, music • A user has an information need and uses an IR system that retrieves the relevant or significant information from a collection of documents 4
  • 5. Information Retrieval Evaluation • IR systems are based on models to estimate relevance, implementing different techniques • How good is my system? What system is better? • Answered with IR Evaluation experiments – “if you can’t measure it, you can’t improve it” – But we need to be able to trust our measurements • Research on IR Evaluation – Improve our methods to evaluate systems – Critical for the correct development of the field 5
  • 6. History of IR Evaluation research MEDLARS Cranfield 2 SMART 1960 SIGIR 1970 1980 1990 2000 2010 6
  • 7. History of IR Evaluation research MEDLARS Cranfield 2 SMART 1960 TREC SIGIR 1970 1980 1990 INEX CLEF NTCIR 2000 2010 6
  • 8. History of IR Evaluation research MEDLARS Cranfield 2 SMART 1960 TREC SIGIR 1970 1980 1990 INEX CLEF NTCIR 2000 2010 ISMIR MIREX MusiCLEF MSD Challenge 6
  • 9. History of IR Evaluation research MEDLARS Cranfield 2 SMART 1960 TREC SIGIR 1970 1980 1990 INEX CLEF NTCIR 2000 2010 ISMIR MIREX MusiCLEF MSD Challenge 6
  • 10. History of IR Evaluation research MEDLARS Cranfield 2 SMART 1960 TREC SIGIR 1970 1980 1990 INEX CLEF NTCIR 2000 2010 ISMIR MIREX MusiCLEF MSD Challenge 6
  • 11. 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 • Annual evaluation in MIREX 7
  • 12. Outline • Introduction – Scope – The Cranfield Paradigm • • • • Validity Reliability Efficiency Conclusions and Future Work 8
  • 13. Outline • Introduction – Scope – The Cranfield Paradigm • • • • Validity Reliability Efficiency Conclusions and Future Work 9
  • 14. 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? 10
  • 15. Measure user experience • We are interested in user-measures – Time to complete task, idle time, success rate, failure rate, frustration, ease to learn, ease to use … – Their distributions describe user experience, fully • User satisfaction is the bigger picture – How likely is it that an arbitrary user, with an arbitrary query (and with an arbitrary document collection) will be satisfied by the system? • This is the ultimate goal: the good, the better 11
  • 16. The Cranfield Paradigm • Estimate user-measure distributions – Sample documents, queries and users – Monitor user experience and behavior – Representativeness, cost, ethics, privacy … • Fix samples to allow reproducibility – But cannot fix users and their behavior – Remove users, but include a static user component, fixed across experiments: ground truth judgments – Still need to include the dynamics of the process: user models behind effectiveness measures and scales 12
  • 17. Test collections • Our goal is the users: user-measure = f(system) • Cranfield measures systems: system-effectiveness = f(system, measure, scale) • Estimators of the distributions of user-measures – Only source of variability is the systems themselves – Reproducibility becomes easy – Experiments are inexpensive (collections are not) – Research becomes systematic 13
  • 18. Validity, Reliability and Efficiency • Validity: are we measuring what we want to? – How well are effectiveness and satisfaction correlated? – How good is good and how better is better? • Reliability: how repeatable are the results? – How large do samples have to be? – What statistical methods should be used? • Efficiency: how inexpensive is it to get valid and reliable results? – Can we estimate results with fewer judgments? 14
  • 19. Goal of this dissertation Study and improve the validity, reliability and efficiency of the methods used to evaluate AMS systems Additionally, improve meta-evaluation methods 15
  • 20. Outline • Introduction – Scope – The Cranfield Paradigm • • • • Validity Reliability Efficiency Conclusions and Future Work 16
  • 21. Outline • Introduction • Validity – System Effectiveness and User Satisfaction – Modeling Distributions • Reliability • Efficiency • Conclusions and Future Work 17
  • 22. Assumption of Cranfield • Systems with better effectiveness are perceived by users as more useful, more satisfactory • But different effectiveness measures and relevance scales produce different distributions – Which one is better to predict user satisfaction? • Map system effectiveness onto user satisfaction, experimentally – If P@10 = 0.2, how likely is it that an arbitrary user will find the results satisfactory? – What if DCG@20 = 0.46? 18
  • 23. Measures and scales Measure P@5 AP@5 RR@5 CGl@5 CGe@5 DCGl@5 DCGe@5 EDCGl@5 EDCGe@5 Ql@5 Qe@5 RBPl@5 RBPe@5 ERRl@5 ERRe@5 GAP@5 ADR@5 Original Broad Fine X X X X X X X X X X X X X X X X X X X X X Artificial Graded nℒ=3 nℒ=4 nℒ=5 X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X ℓmin=20 X X X P@5 P@5 X DCGl@5 X EDCGl@5 AP@5 AP@5 X RBPl@5 X ERRl@5 AP@5 X Artificial Binary ℓmin=40 ℓmin=60 X X X X X X P@5 P@5 P@5 P@5 X X DCGl@5 DCGl@5 X X EDCGl@5 EDCGl@5 AP@5 AP@5 AP@5 AP@5 X X RBPl@5 RBPl@5 X X ERRl@5 ERRl@5 AP@5 AP@5 X X ℓmin=80 X X X P@5 P@5 X DCGl@5 X EDCGl@5 AP@5 AP@5 X RBPl@5 X ERRl@5 AP@5 X 19
  • 24. Measures and scales Measure P@5 AP@5 RR@5 CGl@5 CGe@5 DCGl@5 DCGe@5 EDCGl@5 EDCGe@5 Ql@5 Qe@5 RBPl@5 RBPe@5 ERRl@5 ERRe@5 GAP@5 ADR@5 Original Broad Fine X X X X X X X X X X X X X X X X X X X X X Artificial Graded nℒ=3 nℒ=4 nℒ=5 X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X ℓmin=20 X X X P@5 P@5 X DCGl@5 X EDCGl@5 AP@5 AP@5 X RBPl@5 X ERRl@5 AP@5 X Artificial Binary ℓmin=40 ℓmin=60 X X X X X X P@5 P@5 P@5 P@5 X X DCGl@5 DCGl@5 X X EDCGl@5 EDCGl@5 AP@5 AP@5 AP@5 AP@5 X X RBPl@5 RBPl@5 X X ERRl@5 ERRl@5 AP@5 AP@5 X X ℓmin=80 X X X P@5 P@5 X DCGl@5 X EDCGl@5 AP@5 AP@5 X RBPl@5 X ERRl@5 AP@5 X 19
  • 25. Measures and scales Measure P@5 AP@5 RR@5 CGl@5 CGe@5 DCGl@5 DCGe@5 EDCGl@5 EDCGe@5 Ql@5 Qe@5 RBPl@5 RBPe@5 ERRl@5 ERRe@5 GAP@5 ADR@5 Original Broad Fine X X X X X X X X X X X X X X X X X X X X X Artificial Graded nℒ=3 nℒ=4 nℒ=5 X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X ℓmin=20 X X X P@5 P@5 X DCGl@5 X EDCGl@5 AP@5 AP@5 X RBPl@5 X ERRl@5 AP@5 X Artificial Binary ℓmin=40 ℓmin=60 X X X X X X P@5 P@5 P@5 P@5 X X DCGl@5 DCGl@5 X X EDCGl@5 EDCGl@5 AP@5 AP@5 AP@5 AP@5 X X RBPl@5 RBPl@5 X X ERRl@5 ERRl@5 AP@5 AP@5 X X ℓmin=80 X X X P@5 P@5 X DCGl@5 X EDCGl@5 AP@5 AP@5 X RBPl@5 X ERRl@5 AP@5 X 19
  • 26. Measures and scales Measure P@5 AP@5 RR@5 CGl@5 CGe@5 DCGl@5 DCGe@5 EDCGl@5 EDCGe@5 Ql@5 Qe@5 RBPl@5 RBPe@5 ERRl@5 ERRe@5 GAP@5 ADR@5 Original Broad Fine X X X X X X X X X X X X X X X X X X X X X Artificial Graded nℒ=3 nℒ=4 nℒ=5 X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X ℓmin=20 X X X P@5 P@5 X DCGl@5 X EDCGl@5 AP@5 AP@5 X RBPl@5 X ERRl@5 AP@5 X Artificial Binary ℓmin=40 ℓmin=60 X X X X X X P@5 P@5 P@5 P@5 X X DCGl@5 DCGl@5 X X EDCGl@5 EDCGl@5 AP@5 AP@5 AP@5 AP@5 X X RBPl@5 RBPl@5 X X ERRl@5 ERRl@5 AP@5 AP@5 X X ℓmin=80 X X X P@5 P@5 X DCGl@5 X EDCGl@5 AP@5 AP@5 X RBPl@5 X ERRl@5 AP@5 X 19
  • 27. Measures and scales Measure P@5 AP@5 RR@5 CGl@5 CGe@5 DCGl@5 DCGe@5 EDCGl@5 EDCGe@5 Ql@5 Qe@5 RBPl@5 RBPe@5 ERRl@5 ERRe@5 GAP@5 ADR@5 Original Broad Fine X X X X X X X X X X X X X X X X X X X X X Artificial Graded nℒ=3 nℒ=4 nℒ=5 X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X ℓmin=20 X X X P@5 P@5 X DCGl@5 X EDCGl@5 AP@5 AP@5 X RBPl@5 X ERRl@5 AP@5 X Artificial Binary ℓmin=40 ℓmin=60 X X X X X X P@5 P@5 P@5 P@5 X X DCGl@5 DCGl@5 X X EDCGl@5 EDCGl@5 AP@5 AP@5 AP@5 AP@5 X X RBPl@5 RBPl@5 X X ERRl@5 ERRl@5 AP@5 AP@5 X X ℓmin=80 X X X P@5 P@5 X DCGl@5 X EDCGl@5 AP@5 AP@5 X RBPl@5 X ERRl@5 AP@5 X 19
  • 28. Measures and scales Measure P@5 AP@5 RR@5 CGl@5 CGe@5 DCGl@5 DCGe@5 EDCGl@5 EDCGe@5 Ql@5 Qe@5 RBPl@5 RBPe@5 ERRl@5 ERRe@5 GAP@5 ADR@5 Original Broad Fine X X X X X X X X X X X X X X X X X X X X X Artificial Graded nℒ=3 nℒ=4 nℒ=5 X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X ℓmin=20 X X X P@5 P@5 X DCGl@5 X EDCGl@5 AP@5 AP@5 X RBPl@5 X ERRl@5 AP@5 X Artificial Binary ℓmin=40 ℓmin=60 X X X X X X P@5 P@5 P@5 P@5 X X DCGl@5 DCGl@5 X X EDCGl@5 EDCGl@5 AP@5 AP@5 AP@5 AP@5 X X RBPl@5 RBPl@5 X X ERRl@5 ERRl@5 AP@5 AP@5 X X ℓmin=80 X X X P@5 P@5 X DCGl@5 X EDCGl@5 AP@5 AP@5 X RBPl@5 X ERRl@5 AP@5 X 19
  • 29. Measures and scales Measure P@5 AP@5 RR@5 CGl@5 CGe@5 DCGl@5 DCGe@5 EDCGl@5 EDCGe@5 Ql@5 Qe@5 RBPl@5 RBPe@5 ERRl@5 ERRe@5 GAP@5 ADR@5 Original Broad Fine X X X X X X X X X X X X X X X X X X X X X Artificial Graded nℒ=3 nℒ=4 nℒ=5 X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X ℓmin=20 X X X P@5 P@5 X DCGl@5 X EDCGl@5 AP@5 AP@5 X RBPl@5 X ERRl@5 AP@5 X Artificial Binary ℓmin=40 ℓmin=60 X X X X X X P@5 P@5 P@5 P@5 X X DCGl@5 DCGl@5 X X EDCGl@5 EDCGl@5 AP@5 AP@5 AP@5 AP@5 X X RBPl@5 RBPl@5 X X ERRl@5 ERRl@5 AP@5 AP@5 X X ℓmin=80 X X X P@5 P@5 X DCGl@5 X EDCGl@5 AP@5 AP@5 X RBPl@5 X ERRl@5 AP@5 X 19
  • 30. Measures and scales Measure P@5 AP@5 RR@5 CGl@5 CGe@5 DCGl@5 DCGe@5 EDCGl@5 EDCGe@5 Ql@5 Qe@5 RBPl@5 RBPe@5 ERRl@5 ERRe@5 GAP@5 ADR@5 Original Broad Fine X X X X X X X X X X X X X X X X X X X X X Artificial Graded nℒ=3 nℒ=4 nℒ=5 X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X ℓmin=20 X X X P@5 P@5 X DCGl@5 X EDCGl@5 AP@5 AP@5 X RBPl@5 X ERRl@5 AP@5 X Artificial Binary ℓmin=40 ℓmin=60 X X X X X X P@5 P@5 P@5 P@5 X X DCGl@5 DCGl@5 X X EDCGl@5 EDCGl@5 AP@5 AP@5 AP@5 AP@5 X X RBPl@5 RBPl@5 X X ERRl@5 ERRl@5 AP@5 AP@5 X X ℓmin=80 X X X P@5 P@5 X DCGl@5 X EDCGl@5 AP@5 AP@5 X RBPl@5 X ERRl@5 AP@5 X 19
  • 31. Measures and scales Measure P@5 AP@5 RR@5 CGl@5 CGe@5 DCGl@5 DCGe@5 EDCGl@5 EDCGe@5 Ql@5 Qe@5 RBPl@5 RBPe@5 ERRl@5 ERRe@5 GAP@5 ADR@5 Original Broad Fine X X X X X X X X X X X X X X X X X X X X X Artificial Graded nℒ=3 nℒ=4 nℒ=5 X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X ℓmin=20 X X X P@5 P@5 X DCGl@5 X EDCGl@5 AP@5 AP@5 X RBPl@5 X ERRl@5 AP@5 X Artificial Binary ℓmin=40 ℓmin=60 X X X X X X P@5 P@5 P@5 P@5 X X DCGl@5 DCGl@5 X X EDCGl@5 EDCGl@5 AP@5 AP@5 AP@5 AP@5 X X RBPl@5 RBPl@5 X X ERRl@5 ERRl@5 AP@5 AP@5 X X ℓmin=80 X X X P@5 P@5 X DCGl@5 X EDCGl@5 AP@5 AP@5 X RBPl@5 X ERRl@5 AP@5 X 19
  • 32. Measures and scales Measure P@5 AP@5 RR@5 CGl@5 CGe@5 DCGl@5 DCGe@5 EDCGl@5 EDCGe@5 Ql@5 Qe@5 RBPl@5 RBPe@5 ERRl@5 ERRe@5 GAP@5 ADR@5 Original Broad Fine X X X X X X X X X X X X X X X X X X X X X Artificial Graded nℒ=3 nℒ=4 nℒ=5 X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X ℓmin=20 X X X P@5 P@5 X DCGl@5 X EDCGl@5 AP@5 AP@5 X RBPl@5 X ERRl@5 AP@5 X Artificial Binary ℓmin=40 ℓmin=60 X X X X X X P@5 P@5 P@5 P@5 X X DCGl@5 DCGl@5 X X EDCGl@5 EDCGl@5 AP@5 AP@5 AP@5 AP@5 X X RBPl@5 RBPl@5 X X ERRl@5 ERRl@5 AP@5 AP@5 X X ℓmin=80 X X X P@5 P@5 X DCGl@5 X EDCGl@5 AP@5 AP@5 X RBPl@5 X ERRl@5 AP@5 X 19
  • 33. Measures and scales Measure P@5 AP@5 RR@5 CGl@5 CGe@5 DCGl@5 DCGe@5 EDCGl@5 EDCGe@5 Ql@5 Qe@5 RBPl@5 RBPe@5 ERRl@5 ERRe@5 GAP@5 ADR@5 Original Broad Fine X X X X X X X X X X X X X X X X X X X X X Artificial Graded nℒ=3 nℒ=4 nℒ=5 X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X ℓmin=20 X X X P@5 P@5 X DCGl@5 X EDCGl@5 AP@5 AP@5 X RBPl@5 X ERRl@5 AP@5 X Artificial Binary ℓmin=40 ℓmin=60 X X X X X X P@5 P@5 P@5 P@5 X X DCGl@5 DCGl@5 X X EDCGl@5 EDCGl@5 AP@5 AP@5 AP@5 AP@5 X X RBPl@5 RBPl@5 X X ERRl@5 ERRl@5 AP@5 AP@5 X X ℓmin=80 X X X P@5 P@5 X DCGl@5 X EDCGl@5 AP@5 AP@5 X RBPl@5 X ERRl@5 AP@5 X 19
  • 34. Measures and scales Measure P@5 AP@5 RR@5 CGl@5 CGe@5 DCGl@5 DCGe@5 EDCGl@5 EDCGe@5 Ql@5 Qe@5 RBPl@5 RBPe@5 ERRl@5 ERRe@5 GAP@5 ADR@5 Original Broad Fine Artificial Graded nℒ=3 nℒ=4 nℒ=5 MIREX X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X ℓmin=20 X X X P@5 P@5 X DCGl@5 X EDCGl@5 AP@5 AP@5 X RBPl@5 X ERRl@5 AP@5 X Artificial Binary ℓmin=40 ℓmin=60 X X X X X X P@5 P@5 P@5 P@5 X X DCGl@5 DCGl@5 X X EDCGl@5 EDCGl@5 AP@5 AP@5 AP@5 AP@5 X X RBPl@5 RBPl@5 X X ERRl@5 ERRl@5 AP@5 AP@5 X X ℓmin=80 X X X P@5 P@5 X DCGl@5 X EDCGl@5 AP@5 AP@5 X RBPl@5 X ERRl@5 AP@5 X 19
  • 35. Experimental design 20
  • 36. 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 21
  • 37. Data • Queries, documents and judgments from MIREX • 4115 unique and artificial examples • 432 unique queries, 5636 unique documents • Answers collected via Crowdsourcing – Quality control with trap questions • 113 unique subjects 22
  • 38. Single system: how good is it? • For 2045 examples (49%) users could not decide which system was better What do we expect? 23
  • 39. Single system: how good is it? • For 2045 examples (49%) users could not decide which system was better 23
  • 40. Single system: how good is it? • Large ℓmin thresholds underestimate satisfaction 24
  • 41. Single system: how good is it? • Users don’t pay attention to ranking? 25
  • 42. Single system: how good is it? • Exponential gain underestimates satisfaction 26
  • 43. Single system: how good is it? • Document utility independent of others 27
  • 44. Two systems: which one is better? • For 2090 examples (51%) users did prefer one system over the other one What do we expect? 28
  • 45. Two systems: which one is better? • For 2090 examples (51%) users did prefer one system over the other one 28
  • 46. Two systems: which one is better? • Large differences needed for users to note them 29
  • 47. Two systems: which one is better? • More relevance levels are better to discriminate 30
  • 48. Two systems: which one is better? • Cascade and navigational user models are not appropriate 31
  • 49. Two systems: which one is better? • Users do prefer the (supposedly) worse system 32
  • 50. Summary • Effectiveness and satisfaction are clearly correlated – But there is a bias of 20% because of user disagreement – Room for improvement through personalization • Magnitude of differences does matter – Just looking at rankings is very naive • Be careful with statistical significance – Need Δλ≈0.4 for users to agree with effectiveness • Historically, only 20% of times in MIREX • Differences among measures and scales – Linear gain slightly better than exponential gain – Informational and positional user models better than navigational and cascade – The more relevance levels, the better 33
  • 51. Measures and scales Measure P@5 AP@5 RR@5 CGl@5 CGe@5 DCGl@5 DCGe@5 EDCGl@5 EDCGe@5 Ql@5 Qe@5 RBPl@5 RBPe@5 ERRl@5 ERRe@5 GAP@5 ADR@5 Original Broad Fine X X X X X X X X X X X X X X X X X X X X X Artificial Graded nℒ=3 nℒ=4 nℒ=5 X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X ℓmin=20 X X X P@5 P@5 X DCGl@5 X EDCGl@5 AP@5 AP@5 X RBPl@5 X ERRl@5 AP@5 X Artificial Binary ℓmin=40 ℓmin=60 X X X X X X P@5 P@5 P@5 P@5 X X DCGl@5 DCGl@5 X X EDCGl@5 EDCGl@5 AP@5 AP@5 AP@5 AP@5 X X RBPl@5 RBPl@5 X X ERRl@5 ERRl@5 AP@5 AP@5 X X ℓmin=80 X X X P@5 P@5 X DCGl@5 X EDCGl@5 AP@5 AP@5 X RBPl@5 X ERRl@5 AP@5 X 34
  • 52. Measures and scales Measure P@5 AP@5 RR@5 CGl@5 CGe@5 DCGl@5 DCGe@5 EDCGl@5 EDCGe@5 Ql@5 Qe@5 RBPl@5 RBPe@5 ERRl@5 ERRe@5 GAP@5 ADR@5 Original Broad Fine X X X X X X X X X X X X X X X X X X X X X Artificial Graded nℒ=3 nℒ=4 nℒ=5 X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X ℓmin=20 X X X P@5 P@5 X DCGl@5 X EDCGl@5 AP@5 AP@5 X RBPl@5 X ERRl@5 AP@5 X Artificial Binary ℓmin=40 ℓmin=60 X X X X X X P@5 P@5 P@5 P@5 X X DCGl@5 DCGl@5 X X EDCGl@5 EDCGl@5 AP@5 AP@5 AP@5 AP@5 X X RBPl@5 RBPl@5 X X ERRl@5 ERRl@5 AP@5 AP@5 X X ℓmin=80 X X X P@5 P@5 X DCGl@5 X EDCGl@5 AP@5 AP@5 X RBPl@5 X ERRl@5 AP@5 X 35
  • 53. Outline • Introduction • Validity – System Effectiveness and User Satisfaction – Modeling Distributions • Reliability • Efficiency • Conclusions and Future Work 36
  • 54. Outline • Introduction • Validity – System Effectiveness and User Satisfaction – Modeling Distributions • Reliability • Efficiency • Conclusions and Future Work 37
  • 55. Evaluate in terms of user satisfaction • So far, arbitrary users for a single query – P Sat Ql @5 = 0.61 = 0.7 • Easily for n users and a single query – P Sat15 = 10 Q l @5 = 0.61 = 0.21 • What about a sample of queries 𝒬? – Map queries separately for the distribution of P(Sat) – For easier mappings, P(Sat | λ) functions are interpolated with simple polynomials 38
  • 56. Expected probability of satisfaction • Now we can compute point and interval estimates of the expected probability of satisfaction • Intuition fails when interpreting effectiveness 39
  • 57. System success • If P(Sat) ≥ threshold the system is successful – Setting the threshold was rather arbitrary – Now it is meaningful, in terms of user satisfaction • Intuitively, we want the majority of users to find the system satisfactory – P Succ = P P Sat > 0.5 = 1 − FP Sat (0.5) • Improving queries for which we are bad is worthier than further improving those for which we are already good 40
  • 58. Distribution of P(Sat) • Need to estimate the cumulative distribution function of user satisfaction: FP(Sat) • Not described by a typical distribution family – ecdf converges, but what is a good sample size? – Compare with Normal, Truncated Normal and Beta • Compared on >2M random samples from MIREX collections, at different query set sizes • Goodness of fit as to Cramér-von Mises ω2 41
  • 59. Estimated distribution of P(Sat) • More than ≈25 queries in the collection – ecdf approximates better • Less than ≈25 queries in the collection – Normal for graded scales, ecdf for binary scales • Beta is always the best with the Fine scale • The more levels in the relevance scale, the better • Linear gain better than exponential gain 42
  • 60. Intuition fails, again • Intuitive conclusions based on effectiveness alone contradict those based on user satisfaction – E Δλ = −0.002 – E ΔP Sat – E ΔP Succ = 0.001 = 0.07 43
  • 61. Intuition fails, again • Intuitive conclusions based on effectiveness alone contradict those based on user satisfaction – E Δλ = −0.002 – E ΔP Sat – E ΔP Succ = 0.001 = 0.07 43
  • 62. Intuition fails, again • Intuitive conclusions based on effectiveness alone contradict those based on user satisfaction – E Δλ = −0.002 – E ΔP Sat – E ΔP Succ = 0.001 = 0.07 43
  • 63. Historically, in MIREX • Systems are not as satisfactory as we thought • But they are more successful – Good (or bad) for some kinds of queries 44
  • 64. Measures and scales Measure P@5 AP@5 CGl@5 CGe@5 DCGl@5 DCGe@5 Ql@5 Qe@5 RBPl@5 RBPe@5 GAP@5 Original Broad Fine X X X X X X X X X X X X X X Artificial Graded nℒ=4 nℒ=5 X X X X X X X X X X X X X X X X X X Artificial Binary ℓmin=20 ℓmin=40 X X X X P@5 P@5 P@5 P@5 X X DCGl@5 DCGl@5 AP@5 AP@5 AP@5 AP@5 X X RBPl@5 RBPl@5 AP@5 AP@5 45
  • 65. Measures and scales Measure P@5 AP@5 CGl@5 CGe@5 DCGl@5 DCGe@5 Ql@5 Qe@5 RBPl@5 RBPe@5 GAP@5 Original Broad Fine X X X X X X X X X X X X X X Artificial Graded nℒ=4 nℒ=5 X X X X X X X X X X X X X X X X X X Artificial Binary ℓmin=20 ℓmin=40 X X X X P@5 P@5 P@5 P@5 X X DCGl@5 DCGl@5 AP@5 AP@5 AP@5 AP@5 X X RBPl@5 RBPl@5 AP@5 AP@5 46
  • 66. Outline • Introduction • Validity – System Effectiveness and User Satisfaction – Modeling Distributions • Reliability • Efficiency • Conclusions and Future Work 47
  • 67. Outline • Introduction • Validity • Reliability – Optimality of Statistical Significance Tests – Test Collection Size • Efficiency • Conclusions and Future Work 48
  • 68. Random error • Test collections are just samples from larger, possibly infinite, populations • If we conclude system A is better than B, how confident can we be? – Δλ 𝒬 is just an estimate of the population mean μΔλ • Usually employ some statistical significance test for differences in location • If it is statistically significant, we have confidence that the true difference is at least that large 49
  • 69. Statistical hypothesis testing • Set two mutually exclusive hypotheses – H0 : μΔλ = 0 – H1 : μΔλ ≠ 0 • Run test, obtain p-value= P μΔλ ≥ Δλ 𝒬 H0 – p ≤ α: statistically significant, high confidence – p > α: statistically non-significant, low confidence • Possible errors in the binary decision – Type I: incorrectly reject H0 – Type II: incorrectly accept H0 50
  • 70. Statistical significance tests • (Non-)parametric tests – t-test, Wilcoxon test, Sign test • Based on resampling – Bootstrap test, permutation/randomization test • They make certain assumptions about distributions and sampling methods – Often violated in IR evaluation experiments – Which test behaves better, in practice, knowing that assumptions are violated? 51
  • 71. Optimality criteria • Power – Achieve significance as often as possible (low Type II) – Usually increases Type I error rates • Safety – Minimize Type I error rates – Usually decreases power • Exactness – Maintain Type I error rate at α level – Permutation test is theoretically exact 52
  • 72. Experimental design • Randomly split query set in two • Evaluate all systems with both subsets – Simulating two different test collections • Compare p-values with both subsets – How well do statistical tests agree with themselves? – At different α levels • All systems and queries from MIREX 2007-2011 – >15M p-values 53
  • 73. Power and success • Bootstrap test is the most powerful • Wilcoxon, bootstrap and permutation are the most successful, depending on α level 54
  • 74. Conflicts • Wilcoxon and t-test are the safest at low α levels • Wilcoxon is the most exact at low α levels, but bootstrap is for usual levels 55
  • 75. Optimal measure and scale • Power: CGl@5, GAP@5, DCGl@5 and RBPl@5 • Success: CGl@5, GAP@5, DCGl@5 and RBPl@5 • Conflicts: very similar across measures • Power: Fine, Broad and binary • Success: Fine, Broad and binary • Conflicts: very similar across scales 56
  • 76. Outline • Introduction • Validity • Reliability – Optimality of Statistical Significance Tests – Test Collection Size • Efficiency • Conclusions and Future Work 57
  • 77. Outline • Introduction • Validity • Reliability – Optimality of Statistical Significance Tests – Test Collection Size • Efficiency • Conclusions and Future Work 58
  • 78. Acceptable sample size • Reliability is higher with larger sample sizes – But it is also more expensive – What is an acceptable test collection size? • Answer with Generalizability Theory – G-Study: estimate variance components – D-Study: estimate reliability of different sample sizes and experimental designs 59
  • 79. G-study: variance components • Fully crossed experimental design: s × q λq,A = λ + λA + λq + εqA σ2 = σ2 + σ2 + σ2 s q sq 60
  • 80. G-study: variance components • Fully crossed experimental design: s × q λq,A = λ + λA + λq + εqA σ2 = σ2 + σ2 + σ2 s q sq 60
  • 81. G-study: variance components • Fully crossed experimental design: s × q λq,A = λ + λA + λq + εqA σ2 = σ2 + σ2 + σ2 s q sq 60
  • 82. G-study: variance components • Fully crossed experimental design: s × q λq,A = λ + λA + λq + εqA σ2 = σ2 + σ2 + σ2 s q sq 60
  • 83. G-study: variance components • Fully crossed experimental design: s × q λq,A = λ + λA + λq + εqA σ2 = σ2 + σ2 + σ2 s q sq 60
  • 84. G-study: variance components • Fully crossed experimental design: s × q λq,A = λ + λA + λq + εqA σ2 = σ2 + σ2 + σ2 s q sq 60
  • 85. G-study: variance components • Fully crossed experimental design: s × q λq,A = λ + λA + λq + εqA σ2 = σ2 + σ2 + σ2 s q sq 60
  • 86. G-study: variance components • Fully crossed experimental design: s × q λq,A = λ + λA + λq + εqA σ2 = σ2 + σ2 + σ2 s q sq 60
  • 87. G-study: variance components • Fully crossed experimental design: s × q λq,A = λ + λA + λq + εqA σ2 = σ2 + σ2 + σ2 s q sq 60
  • 88. G-study: variance components • Fully crossed experimental design: s × q λq,A = λ + λA + λq + εqA σ2 = σ2 + σ2 + σ2 s q sq 60
  • 89. G-study: variance components • Fully crossed experimental design: s × q λq,A = λ + λA + λq + εqA σ2 = σ2 + σ2 + σ2 s q sq 60
  • 90. G-study: variance components • Fully crossed experimental design: s × q λq,A = λ + λA + λq + εqA σ2 = σ2 + σ2 + σ2 s q sq • Estimated with Analysis of Variance • If σ2 is small or σ2 is large, we need more queries s q 60
  • 91. D-study: variance ratios • Stability of absolute scores Φ nq σ2 s = σ2 + σ2 q e 2 σs + nq • Stability of relative scores Eρ2 nq = σ2 s σ2 σ2 + e s nq • We can easily estimate how many queries are needed to reach some level of stability (reliability) 61
  • 92. D-study: variance ratios • Stability of absolute scores Φ nq σ2 s = σ2 + σ2 q e 2 σs + nq • Stability of relative scores Eρ2 nq = σ2 s σ2 σ2 + e s nq • We can easily estimate how many queries are needed to reach some level of stability (reliability) 61
  • 93. Effect of query set size • • • • Average absolute stability Φ = 0.97 ≈65 queries needed for Φ2 = 0.95, ≈100 in worst cases Fine scale slightly better than Broad and binary scales RBPl@5 and nDCGl@5 are the most stable 62
  • 94. Effect of query set size • • • • Average relative stability Eρ2 = 0.98 ≈35 queries needed for Eρ2 = 0.95, ≈60 in worst cases Fine scale better than Broad and binary scales CGl@5 and RBPl@5 are the most stable 63
  • 95. Effect of cutoff k • What if we use a deeper cutoff, k=10? – From 100 queries and k=5 to 50 queries and k=10 – Should still have stable scores – Judging effort should decrease – Rank-based measures should become more stable • Tested in MIREX 2012 – Apparently in 2013 too 64
  • 96. Effect of cutoff k • Judging effort reduced to 72% of the usual • Generally stable – From Φ = 0.81 to Φ = 0.83 – From Eρ2 = 0.93 to Eρ2 = 0.95 65
  • 97. Effect of cutoff k • Reliability given a fixed budged for judging? – k=10 allows us to use fewer queries, about 70% – Slightly reduced relative stability 66
  • 98. Effect of assessor set size • More assessors or simply more queries? – Judging effort is multiplied • Can be studied with MIREX 2006 data – 3 different assessors per query – Nested experimental design: s × h: q 67
  • 99. Effect of assessor set size • 2 2 Broad scale: σs ≈ σh:q Fine scale: σ2 ≫ σ2 s h:q • • Always better to spend resources on queries 68
  • 100. Summary • MIREX collections generally larger than necessary • For fixed budget – More queries better than more assessors – More queries slightly better than deeper cutoff • Worth studying alternative user model? • • • • Employ G-Theory while building the collection Fine better than Broad, better than binary CGl@5 and DCGl@5 best for relative stability RBPl@5 and nDCGl@5 best for absolute stability 69
  • 101. Outline • Introduction • Validity • Reliability – Optimality of Statistical Significance Tests – Test Collection Size • Efficiency • Conclusions and Future Work 70
  • 102. Outline • • • • Introduction Validity Reliability Efficiency – Learning Relevance Distributions – Low-cost Evaluation • Conclusions and Future Work 71
  • 103. Probabilistic evaluation • The MIREX setting is still expensive – Need to judge all top k documents from all systems – Takes days, even weeks sometimes • Model relevance probabilistically • Relevance judgments are random variables over the space of possible assignments of relevance • Effectiveness measures are also probabilistic 72
  • 104. Probabilistic evaluation • Accuracy increases as we make judgments – E R d ← rd • Reliability increases too (confidence) – Var R d ← 0 • Iteratively estimate relevance and effectiveness – If confidence is low, make judgments – If confidence is high, stop • Judge as few documents as possible 73
  • 105. Learning distributions of relevance • Uniform distribution is very uninformative • Historical distribution in MIREX has high variance • Estimate from a set of features: P R d = ℓ θd – For each document separately – Ordinal Logistic Regression • Three sets of features – Output-based, can always be used – Judgment-based, to exploit known judgments – Audio-based, to exploit musical similarity 74
  • 106. Learned models • Mout : can be used even without judgments – Similarity between systems’ outputs – Genre and artist metadata • Genre is highly correlated to similarity – Decent fit, R2 ≈ 0.35 • Mjud : can be used when there are judgments – Similarity between systems’ outputs – Known relevance of same system and same artist • Artist is extremely correlated to similarity – Excellent fit, R2 ≈ 0.91 75
  • 107. Estimation errors • Actual vs. predicted by Mout – 0.36 with Broad and 0.34 with Fine • Actual vs. predicted by Mjud – 0.14 with Broad and 0.09 with Fine • Among assessors in MIREX 2006 – 0.39 with Broad and 0.31 with Fine • Negligible under the current MIREX setting 76
  • 108. Outline • • • • Introduction Validity Reliability Efficiency – Learning Relevance Distributions – Low-cost Evaluation • Conclusions and Future Work 77
  • 109. Outline • • • • Introduction Validity Reliability Efficiency – Learning Relevance Distributions – Low-cost Evaluation • Conclusions and Future Work 78
  • 110. Probabilistic effectiveness measures • Effectiveness scores are also random variables • Different approaches to compute estimates – Deal with dependence of random variables – Different definitions of confidence • For measures based on ideal ranking (nDCGl@k and RBPl@k) we do not have a closed form – Approximated with Delta method and Taylor series 79
  • 111. Ranking without judgments 1. Estimate relevance with Mout 2. Estimate relative differences and rank systems • Average confidence in the rankings is 94% • Average accuracy of the ranking is 92% 80
  • 112. Ranking without judgments • Can we trust individual estimates? – Ideally, we want X% accuracy when X% confidence – Confidence slightly overestimated in [0.9, 0.99) Confidence [0.5, 0.6) [0.6, 0.7) [0.7, 0.8) [0.8, 0.9) [0.9, 0.95) [0.95, 0.99) [0.99, 1) E[Accuracy] DCGl@5 Broad In bin Accuracy 23 (6.5%) 0.826 14 (4%) 0.786 14 (4%) 0.571 22 (6.2%) 0.864 23 (6.5%) 0.87 24 (6.8%) 0.917 232 (65.9%) 0.996 0.938 Fine In bin 22 (6.2%) 16 (4.5%) 11 (3.1%) 21 (6%) 19 (5.4%) 27 (7.7%) 236 (67%) Accuracy 0.636 0.812 0.364 0.762 0.895 0.926 0.996 0.921 81
  • 113. Relative estimates with judgments 1. Estimate relevance with Mout 2. Estimate relative differences and rank systems 3. While confidence is low (<95%) 1. Select a document and judge it 2. Update relevance estimates with Mjud when possible 3. Update estimates of differences and rank systems • What documents should we judge? – Those that are the most informative – Measure-dependent 82
  • 114. Relative estimates with judgments • Judging effort dramatically reduced – 1.3% with CGl@5, 9.7% with RBPl@5 • Average accuracy still 92%, but improved individually – 74% of estimates with >99% confidence, 99.9% accurate – Expected accuracy improves slightly from 0.927 to 0.931 83
  • 115. Absolute estimates with judgments 1. Estimate relevance with Mout 2. Estimate absolute effectiveness scores 3. While confidence is low (expected error >±0.05) 1. Select a document and judge it 2. Update relevance estimates with Mjud when possible 3. Update estimates of absolute effectiveness scores • What documents should we judge? – Those that reduce variance the most – Measure-dependent 84
  • 116. Absolute estimates with judgments • The stopping condition is overly confident – Virtually no judgments are even needed (supposedly) • But effectiveness is highly overestimated – Especially with nDCGl@5 and RBPl@5 – Mjud, and especially Mout, tend to overestimate relevance 85
  • 117. Absolute estimates with judgments • Practical fix: correct variance • Estimates are better, but at the cost of judging – Need between 15% and 35% of judgments 86
  • 118. Summary • Estimate ranking of systems with no judgments – 92% accuracy on average, trustworthy individually – Statistically significant differences are always correct • If we want more confidence, judge documents – As few as 2% needed to reach 95% confidence – 74% of estimates have >99% confidence and accuracy • Estimate absolute scores, judging as necessary – Around 25% needed to ensure error <0.05 87
  • 119. Outline • • • • Introduction Validity Reliability Efficiency – Learning Relevance Distributions – Low-cost Evaluation • Conclusions and Future Work 88
  • 120. Outline • • • • • Introduction Validity Reliability Efficiency Conclusions and Future Work – Conclusions – Future Work 89
  • 121. Validity • Cranfield tells us about systems, not about users • Provide empirical mapping from system effectiveness onto user satisfaction • Room for personalization quantified in 20% • Need large differences for users to note them • Consider full distributions, not just averages • Conclusions based on effectiveness tend to contradict conclusions based on user satisfaction 90
  • 122. Reliability • Different significance tests for different needs – Bootstrap test is the most powerful – Wilcoxon and t-test are the safest – Wilcoxon and bootstrap test are the most exact • • • • • Practical interpretation of p-values MIREX collections generally larger than needed Spend resources on queries, not on assessors User models with deeper cutoffs are feasible Employ G-Theory while building collections 91
  • 123. Efficiency • • • • • Probabilistic evaluation reduces cost, dramatically Two models to estimate document relevance System rankings 92% accurate without judgments 2% of judgments to reach 95% confidence 25% of judgments to reduce error to 0.05 92
  • 124. Measures and scales • Best measure and scale depends on situation • But generally speaking – CGl@5, DCGl@5 and RBPl@5 – Fine scale – Model distributions as Beta 93
  • 125. Outline • • • • • Introduction Validity Reliability Efficiency Conclusions and Future Work – Conclusions – Future Work 94
  • 126. Outline • • • • • Introduction Validity Reliability Efficiency Conclusions and Future Work – Conclusions – Future Work 95
  • 127. Validity • User studies to understand user behavior • What information to include in test collections • Other forms of relevance judgment to better capture document utility • Explicitly define judging guidelines • Similar mapping for Text IR 96
  • 128. Reliability • Corrections for Multiple Comparisons • Methods to reliably estimate reliability while building test collections 97
  • 129. Efficiency • Better models to estimate document relevance • Correct variance when having just a few relevance judgments available • Estimate relevance beyond k=5 • Other stopping conditions and document weights 98
  • 130. Conduct similar studies for the wealth of tasks in Music Information Retrieval 99
  • 131. Evaluation in Audio Music Similarity PhD dissertation by Julián Urbano Picture by Javier García Leganés, October 3rd 2013