Upgrade to Pro
— share decks privately, control downloads, hide ads and more …
Speaker Deck
Features
Speaker Deck
PRO
Sign in
Sign up for free
Search
Search
Counterfactual learning to rank: introduction
Search
Daiki Tanaka
May 02, 2020
Research
0
730
Counterfactual learning to rank: introduction
一般的なランキング学習からcounterfactual LTRへの導入
Daiki Tanaka
May 02, 2020
Tweet
Share
More Decks by Daiki Tanaka
See All by Daiki Tanaka
カーネル法概観
daikitanak
0
570
カーネル法:正定値カーネルの理論
daikitanak
0
65
[Paper reading] L-SHAPLEY AND C-SHAPLEY: EFFICIENT MODEL INTERPRETATION FOR STRUCTURED DATA
daikitanak
1
180
[Paper Reading] Attention is All You Need
daikitanak
0
110
Interpretability of Machine Learning : Paper reading (LIME)
daikitanak
0
150
[Paper reading] Local Outlier Detection With Interpretation
daikitanak
0
64
Other Decks in Research
See All in Research
問いを起点に、社会と共鳴する知を育む場へ
matsumoto_r
PRO
0
610
スキマバイトサービスにおける現場起点でのデザインアプローチ
yoshioshingyouji
0
230
まずはここから:Overleaf共同執筆・CopilotでAIコーディング入門・Codespacesで独立環境
matsui_528
2
500
SegEarth-OV: Towards Training-Free Open-Vocabulary Segmentation for Remote Sensing Images
satai
3
180
不確実性下における目的と手段の統合的探索に向けた連続腕バンディットの応用 / iot70_gp_rff_mab
monochromegane
2
170
多言語カスタマーインタビューの“壁”を越える~PMと生成AIの共創~ 株式会社ジグザグ 松野 亘
watarumatsuno
0
120
最適決定木を用いた処方的価格最適化
mickey_kubo
4
1.9k
【輪講資料】Moshi: a speech-text foundation model for real-time dialogue
hpprc
3
670
電力システム最適化入門
mickey_kubo
1
920
AlphaEarth Foundations: An embedding field model for accurate and efficient global mapping from sparse label data
satai
1
210
Panopticon: Advancing Any-Sensor Foundation Models for Earth Observation
satai
1
100
近似動的計画入門
mickey_kubo
4
1k
Featured
See All Featured
Designing for humans not robots
tammielis
253
25k
Code Reviewing Like a Champion
maltzj
525
40k
4 Signs Your Business is Dying
shpigford
184
22k
ピンチをチャンスに:未来をつくるプロダクトロードマップ #pmconf2020
aki_iinuma
127
53k
[Rails World 2023 - Day 1 Closing Keynote] - The Magic of Rails
eileencodes
36
2.5k
Connecting the Dots Between Site Speed, User Experience & Your Business [WebExpo 2025]
tammyeverts
8
530
Testing 201, or: Great Expectations
jmmastey
45
7.7k
We Have a Design System, Now What?
morganepeng
53
7.8k
VelocityConf: Rendering Performance Case Studies
addyosmani
332
24k
For a Future-Friendly Web
brad_frost
180
9.9k
Build your cross-platform service in a week with App Engine
jlugia
231
18k
Intergalactic Javascript Robots from Outer Space
tanoku
272
27k
Transcript
Unbiased Learning to Rank May 7, 2020
Learning to rank ઃఆ Supervised LTR Pointwise loss Pairwise loss
Listtwise loss Counterfactual Learning to Rank Counterfactual Evaluation Inverse Propensity Scoring Propensity-weighted Learning to Rank 2
Learning to rank: ઃఆ ೖྗɿ จॻͷू߹ D ग़ྗɿ จॻͷॱҐ R
= (R1; R2; R3:::) ͨͩ͠ɺ֤จॻʹϞσϧ f„ ʹΑͬͯείΞ͕͍͍ͭͯͯ f„ (R1) – f„ (R2) – f„ (R3) ::: ͱͳ͍ͬͯΔɻ(ߴ͍είΞ͕͚ΒΕΔ΄ͲॱҐ͕ߴ͍) Learning to Rank (LTR) ͷత࠷దͳॱҐΛग़ྗ͢ΔϞσϧ f„ ͷύϥϝʔλ „ Λ σʔλ͔ΒٻΊΔ͜ͱɻ 3
Supervised LTR ڭࢣ͋Γ LTR Ͱɺ › ݕࡧΫΤϦ › จॻू߹ ›
ॱҐͷϥϕϧ ΛؚΉσʔληοτΛͬͯϞσϧύϥϝʔλΛٻΊΔɻ ڭࢣ͋Γ LTR Ͱ༻͍ΒΕΔଛࣦओʹ 3 ͭɿ › Pointwise loss › Pairwise loss › Listwise loss y (d) ʹΑͬͯɺจॻ d ͷݕࡧΫΤϦͷؔ࿈Λද͢ͱ͢Δɻ(େ͖͍΄ͲॱҐͷ্Ґʹ ͖ͯཉ͍͠) 4
Pointwise loss Pointwise loss ɺॱҐͷਪఆΛྨɾճؼͱͯ͠ղ͘ɻྫ͑ɺ௨ৗͷճؼଛࣦ (squared loss) ͱͯ͠ҎԼͷΑ͏ʹ༩͑Δɿ Lpointwise :=
1 N N X i=1 (f„ (di) ` y (di))2 Pointwise loss ͷɺϞσϧͷग़ྗΛॱҐͱͯ͠͏͜ͱΛߟྀʹೖΕ͍ͯͳ͍͜ ͱɻLTR Ͱग़ྗͱͯ͠ಘΒΕΔείΞΛฒͼସ͑ͯಘΒΕΔॱҐʹͷΈؔ৺͕͋Δɻ 5
Pairwise loss Pairwise loss Ͱɺ2 ͭͷจॻؒͷ૬ରతͳείΞͷେখΛߟྀʹ͍ΕΔɻྫ͑ɺҎԼ ͷΑ͏ͳ hinge-loss Λ༩͑Δʀ Lpairwise
:= X y(di)>y(dj) max (0; 1 ` (f„ (di) ` f„ (di))): ॱҐ͕૬ରతʹߴ͍จॻείΞ͕ߴ͘ɺॱҐ͕͍จॻείΞΛ͘͢Δؾ࣋ͪɻ Pairwise loss ͷɺશͯͷهࣄϖΞΛಉ༷ʹѻ͏͜ͱɻ࣮ͦͯ͠༻্ top100 ͱ top10 ޙऀͷํ͕ॏࢹ͞ΕΔ͜ͱɻPairwise loss Ͱ top100 ͷԼͷํͷॱҐΛվળ ͤ͞ΔͨΊʹ্ҐͷॱҐΛ٘ਜ਼ʹ͢Δ͜ͱ͕͋Γ͑ͯ͠·͏ɻ 6
Listwise loss Listwise loss ͰॱҐࢦඪΛ࠷దԽ͢Δɻ՝ɺॱҐࢦඪ͕ඍՄೳͰͳ͍͜ͱɻ ྫ͑ɺDCG ɿ DCG = N
X i=1 y (di) log2 (rank (di) + 1) Ͱ͋Δ͕ɺlog2 (rank (di) + 1) ඍෆՄೳͰ͋Δɻ ͦͷͨΊʹ֬తۙࣅΛ༻͍Δํ๏ (ListNetɺListMLE) ɺώϡʔϦεςΟοΫॱҐ ࢦඪͷόϯυΛ࠷దԽ͢Δख๏͕͋Δɻ(LambdaRankɺLambdaLoss) ྫ͑ɺ LambdaRank ͷଛࣦ DCG ͷόϯυͱͳ͍ͬͯΔɿ LLambdaRank := X y(di)>y(dj) log (1 + exp (f„ (dj) ` f„ (di))) j´DCGj 7
ҼՌධՁ తɿ৽͍͠ϥϯΩϯάؔ f„ ΛɺผͷϥϯΩϯάؔ fdeploy ͷԼͰूΊΒΕͨաڈ ͷσʔλ (ΫϦοΫσʔλͳͲ) ΛͬͯධՁ͍ͨ͠ɻ ҎԼͷ
2 ͭͷ߹ʹ͍ͭͯߟ͑Δɻ › શͯͷจॻʹ͍ͭͯਅͷؔ࿈ y (di) ͕طͰ͋Δ࣌ › y (di) Θ͔Βͳ͍͕ɺΫϦοΫใͳͲͷ҉తͳϑΟʔυόοΫͷΈར༻Մೳͳ࣌ 8
ҼՌධՁɿϥϕϧ͕طͳΒશʹධՁ͕Ͱ͖Δ શͯͷจॻʹ͍ͭͯਅͷϥϕϧ y (di) ͕طͰ͋Δ࣌ɺIR(ใݕࡧ) ࢦඪΛܭࢉͰ͖Δɿ ´ (f„; D; y)
= X di2D – (rank (di j f„; D)) ´ y (di) ͜͜Ͱɺ– ॱҐॏΈ͚ؔͰ͋ͬͯɺྫ͑ɿ APR: – (r) = r DCG: – (r) = 1 log2 (1+r) ͳͲ͕༻͍ΒΕΔɻ 9
ҼՌධՁ y (di) Θ͔Βͳ͍͕ɺΫϦοΫใͳͲͷ҉తͳϑΟʔυόοΫͷΈར༻Մೳͳ࣌ɿ › ͋Δจॻʹର͢ΔΫϦοΫɺͦͷจॻ͕ؔ࿈͍ͯ͠Δ͜ͱΛࣔ͢ɺόΠΞεɾϊΠζ ͖ͭͷࢦඪʹͳ͍ͬͯΔɻ › ΫϦοΫ͞Εͳ͔͔ͬͨΒͱ͍ͬͯͦͷจॻ͕ؔͳ͍Θ͚Ͱͳ͍ɻ(จॻ͕ؔͳ ͍ɾϢʔβ͕จॻΛ؍ଌ͍ͯ͠ͳ͍ɾϥϯμϜཁૉʹΑΔͷ)
ଟ͘ͷ؍ଌσʔλʹ͍ͭͯฏۉΛऔΕϊΠζআڈͰ͖Δͱߟ͑ΒΕΔ͕ɺόΠΞεআ ڈͰ͖ͳ͍ɻ 10
ҼՌධՁɿ؍ଌɾΫϦοΫϞσϧ Ϣʔβͷ؍ଌٴͼจॻͷؔ࿈ͷΈΛߟྀʹೖΕΔͱɺϢʔβͷΫϦοΫҎԼͷΑ͏ʹϞ σϦϯάͰ͖ͦ͏ɿ › ϥϯΩϯά R ʹ͓͍ͯจॻ di ͕؍ଌ͞ΕΔ (oi
= 1 Ͱද͢) ֬ɺ P (oi = 1 j R; di) (؍ଌ͞ΕΔ֬ؔ࿈ʹؔͳ͍ͱԾఆ͍ͯ͠Δɻ) › ؔ࿈ y (di) ͱ؍ଌ oi ͕༩͑ΒΕͨ࣌ͷɺจॻ di ͕ΫϦοΫ͞ΕΔ֬ (ci = 1 Ͱද͢) ɺ P (ci = 1 j oi; y (di)) › ΫϦοΫ؍ଌ͞Εͨจॻʹ͔͠ى͜Βͳ͍ͨΊɺϥϯΩϯά R ʹ͓͍ͯΫϦοΫ͞ ΕΔ֬ɿ P (ci = 1 ^ oi = 1 j y (di) ; R) = P (ci = 1 j oi = 1; y (di)) ´ P (oi = 1 j R; di) 11
ҼՌධՁɿ´ (f„; D; y) ͷφΠʔϒਪఆ ´ (f„; D; y) ΛφΠʔϒʹਪఆ͢ΔʹɺΫϦοΫͷใ
(ci) Λਅͷؔ࿈ϥϕϧ (y (di)) ͷΘΓʹ͑Αͯ͘ɺ ´NAIVE (f„; D; c) := X di2D – (rank (di j f„; D)) ´ ci ͱͳΔɻ ΫϦοΫʹϊΠζ͕͍ͬͯͳ͍࣌ɺͭ·Γ P (ci = 1 j oi = 1; y (di)) = y (di) Ͱ͋Δ࣌Ͱ͑͞ɺφΠʔϒਪఆ؍ଌόΠΞεΛड͚͍ͯΔɿ Eo ˆ´NAIVE (f„; D; c)˜ = Eo 2 4 X di2D – (rank (di j f„; D)) ´ ci 3 5 = Eo 2 6 4 X di:oi=1^y(di)=1 – (rank (di j f„; D)) 3 7 5 = X di:y(di)=1 P (oi = 1 j R; di)– (rank (di j f„; D)) = X di2D P (oi = 1 j R; di)– (rank (di j f„; D)) ´ y (di) 12
ҼՌධՁɿ´ (f„; D; y) ͷφΠʔϒਪఆ φΠʔϒਪఆɿ Eo ˆ´NAIVE (f„; D;
c)˜ = X di:y(di)=1 P (oi = 1 j R; di)– (rank (di j f„; D)) ͰɺͦΕͧΕͷจॻͷɺϩάऩू࣌ͷϥϯΩϯά R Ͱͷ؍ଌ֬ͰॏΈͨ͠ਪఆʹͳͬ ͯ͠·͏ɻ ϥϯΩϯάͰɺߴॱҐͷจॻ΄Ͳ؍ଌ͞Ε͍͢ɿ͜ΕΛ position bias ͱݺͿɻϩάऩ ूͷࡍʹߴॱҐʹදࣔ͞Εͨจॻਅͷؔ࿈ΑΓؔ࿈͕͋ΔɺͱόΠΞεΛड͚ͯ͠· ͏ɻ όΠΞεΛআڈ͢ΔͨΊʹɺP (oi = 1 j R; di) Λਪఆ͠ɺิਖ਼ͯ͋͛͠Εྑͦ͞͏ ! είΞʹΑΔόΠΞεআڈ 13
είΞΛ༻͍ͨόΠΞεআڈ Inverse Propensity Scoring(IPS) ʹΑͬͯόΠΞεΛআڈ͢Δɿ ´IPS (f„; D; c) :=
X di2D – (rank (di j f„; D)) P (oi = 1 j R; di) ´ ci ͜͜ͰɺP (oi = 1 j R; di) ϩάऩूதʹදࣔ͞ΕͨϥϯΩϯά R Ͱจॻ di ͕؍ଌ͞ ΕΔ֬Ͱ͋Δɻ´IPS (f„; D; c) ΫϦοΫϊΠζ͕ͳ͍߹ɺͭ·Γ P (ci = 1 j oi = 1; y (di)) = y (di) Ͱ͋Δ࣌ʹ ´ (f„; D; y) ͷෆภਪఆྔͰ͋Δɿ Eo ˆ´IPS (f„; D; c)˜ = Eo 2 4 X di2D – (rank (di j f„; D)) P (oi = 1 j R; di) ´ ci 3 5 = Eo 2 6 4 X di:oi=1^y(di)=1 – (rank (di j f„; D)) P (oi = 1 j R; di) 3 7 5 = X di:y(di)=1 P (oi = 1 j R; di) ´ – (rank (di j f„; D)) P (oi = 1 j R; di) = X di2D – (rank (di j f„; D)) ´ y (di) = ´ (f„; D; y) : 14
Propensity-weighted LTR IPS ´ (f„; D; y) ͷෆภਪఆͰ͋ͬͨɻΑͬͯɺ࠷దͳϞσϧύϥϝʔλ „
IPS Λ ࠷దԽ͢Δ͜ͱͰٻΊΔ͜ͱ͕Ͱ͖ΔɻIPS Λ࠷దԽ͢ΔࡍɺϥϯΩϯάࢦඪ – (r) ͷඍ ෆՄೳੑʹରॲ͢ΔͨΊɺ– (r) ͷ bound Λར༻͢Δɻ Propensity-weighted LTR ͷྲྀΕɿ › ΫϦοΫͷείΞΛਪఆɿ P (oi = 1 j R; di) › ෆภਪఆྔ ´IPS (f„; D; c) ͷ bound ʹ͍ͭͯඍΛܭࢉɿ „0 = r„ "– (rank (di j f„; D)) P (oi = 1 j R; di) # › ϞσϧύϥϝʔλΛߋ৽ „new „old ` „0 15
References › https://ilps.github.io/webconf2020-tutorial-unbiased-ltr/ 16