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
Offline A/B testing for Recommender Systems
Search
Sponsored
·
Your Podcast. Everywhere. Effortlessly.
Share. Educate. Inspire. Entertain. You do you. We'll handle the rest.
→
alpicola
November 20, 2018
Technology
2.3k
0
Share
Embed
Copy iframe code
Copy JS code
Copy link
Start on current slide
Offline A/B testing for Recommender Systems
alpicola
November 20, 2018
More Decks by alpicola
See All by alpicola
[AEON TECH HUB #24] お客様の長期的興味の理解に向けて
alpicola
0
240
商品レコメンドでのexplicit negative feedbackの活用
alpicola
2
1k
Recommending What Video to Watch Next: A Multitask Ranking System
alpicola
1
980
Kibanaを用いたアクセスログ調査と解析 / Access Log Analysis Using Kibana
alpicola
0
1.1k
Other Decks in Technology
See All in Technology
PfEingのアプローチで働こう
rindrics
0
200
Where Is JetBrains AI Heading- — Central CLI, Air Alpha, and the Agentic Development Stack
x5gtrn
PRO
0
150
Redmine 7.0で私が開発した新機能の狙いと背景
vividtone
1
120
ブラウザアプリの継続的パフォーマンスモニタリング (序) / Continuous Browser Application Performance Monitoring; Act 1
moznion
0
110
Podは生きているのにGoだけが落ちる:GOGCとGOMEMLIMITで追うInvisible OOM Killの謎
tkc66buzz
0
180
Adaptive Warehouse を今すぐ導入すべき理由と迷ったときの判断基準
__allllllllez__
0
120
AIに丸投げしないトイル削減 / Eliminating Toil Without Leaving It All to AI
kohbis
4
1k
Code4Lib JAPANカンファレンス2026 開会挨拶 / Code4Lib JAPAN Conference 2026: Opening Remarks
ykiyota
0
200
AI時代におけるプロダクト横断勉強会の設計
zozotech
PRO
0
170
Bet AI Day 2026丨AIによって本質に戻るシステムリスク管理
layerx
PRO
0
1.3k
module Synths; end
asonas
1
130
enechainの内製セルフサービスプラットフォーム
hiyosi
0
140
Featured
See All Featured
SEO Brein meetup: CTRL+C is not how to scale international SEO
lindahogenes
1
2.9k
Leo the Paperboy
mayatellez
8
2.2k
sira's awesome portfolio website redesign presentation
elsirapls
0
410
How to optimise 3,500 product descriptions for ecommerce in one day using ChatGPT
katarinadahlin
PRO
2
3.8k
Thoughts on Productivity
jonyablonski
76
5.4k
Paper Plane (Part 1)
katiecoart
PRO
1
11k
What does AI have to do with Human Rights?
axbom
PRO
1
2.4k
Primal Persuasion: How to Engage the Brain for Learning That Lasts
tmiket
0
440
Agile that works and the tools we love
rasmusluckow
331
22k
My Coaching Mixtape
mlcsv
0
310
Being A Developer After 40
akosma
91
590k
Building Applications with DynamoDB
mza
96
7.2k
Transcript
Offline A/B testing for Recommender Systems ͯͳ ాத (alpicola) @
จಡΈձ 11/19 1
Offline A/B testing for Recommender Systems — CriteoͷWSDM'18ͷจ — SpotifyͷRecSys'18จͰݴٴ
2
Offline A/B testing for Recommender Systems — CriteoͷWSDM'18ͷจ — SpotifyͷRecSys'18จͰݴٴ
— ΫοΫύου։࠵ͷಡΈձͰ͢Ͱʹհ͞Ε͍ͯͨ — ͕ɺվΊͯ۷ΓԼ͕͛ͨͰ͖Εͱࢥ͍·͢ 3
ΦϑϥΠϯABςετ? — ΦϯϥΠϯͰߦ͏ABςετ࣌ؒͱ͕͔͔ۚΔ — ΦϑϥΠϯͰͦΕʹ͍ۙධՁ͕ߦ͑ΕΞϧΰϦζ ϜվળͷαΠΫϧΛߴԽͰ͖Δ — Ͱਫ਼? ! 4
ϩάʹجͮ͘ΦϑϥΠϯධՁͷݚڀ — Counterfactual estimationͱ͔off-policy estimationͱ ݺΕΔ — WSDM'15ͷνϡʔτϦΞϧ — SIGIR'16ͷνϡʔτϦΞϧ
— ධՁ͚ͩͰͳֶ͘शͷతؔʹ͏͜ͱͰ͖Δ — ͜ͷจͰධՁͷΈΛѻ͏ 5
จͷߩݙ — ΦϑϥΠϯABςετͰ༻͍Δใुͷਪఆख๏NCISͷ ͋Δछͷ࠷దੑΛࣔ͢ — ͜ͷݟʹج͍ͮͯNCISͷ֦ுPieceNCISͱ PointNCISΛఏҊ — ΦϯϥΠϯABςετ݁Ռͱͷ૬͕ؔେ্͖͘ 6
ઃఆ — Top-k ϥϯΩϯά — : ϩά — : ίϯςΩετ
— : ΞΫγϣϯ — : ใु 7
ઃఆ — : ίϯςΩετ͔ΒΞΫγϣϯΛબͿϙϦγʔ — : ݱߦͷϙϦγʔ — : ςετ͍ͨ͠ϙϦγʔ
— : ฏۉॲஔޮՌ — ͜ΕΛਪఆ͍ͨ͠ 8
ઃఆ — ΦϯϥΠϯABςετ — ͷݩͰͷϩάͱ ͷݩͰͷϩά͕͋Δ — ඪຊฏۉͰ , ͦΕͧΕਪఆ
— ΦϑϥΠϯABςετ — ͷݩͰͷϩά͔Β ਪఆ ! 9
ैདྷख๏ — Importance sampling (IS) — Normalized importance sampling (NIS)
— Doubly robust estimator (DR) — Capped importance sampling (CIS) — Normalized capped importance sampling (NCIS) ౷ܭϞϯςΧϧϩ๏ͷจ຺Ͱొ 10
Importance sampling (IS) — ! όΠΞε͕ͳ͍ — — " ʹΑΔߴόϦΞϯε
(unbounded) — όϦΞϯε͕େ͖͍ͱ ͱ ΛൺֱͰ͖ͳ͍ 11
Normalized importance sampling (NIS) Λͬͯ Λஔ͖͑ — ! ҰகਪఆྔʹͳΔ —
— " ґવͱͯ͠όϦΞϯεେ 12
Capped importance sampling (CIS) ॏΈͷ࠷େΛ ʹ (max capping) ॏΈ͕ Ҏ্ͷ߲ࣺͯΔ
(zero capping) 13
CISͷόΠΞε 14
CISͷόΠΞε — όΠΞε ͷ࣌ͷ Ͱbound͞ΕΔ — — ใु͕େ͖͍ͱ͜ΖΛऔΕΔΑ͏ʹվળ͍ͨ͠ ͕ͦ͏͢ΔͱόΠΞε͕େ͖͘ͳΔ !
15
CISͷόΠΞε Cappingͷઃఆʹ͍͍τϨʔυΦϑ͕ଘࡏ͠ͳ͍ ! 16
Normalized capped importance sampling (NCIS) NIS, CIS྆ํͷΞΠσΞΛ࣋ͪࠐΉ 17
NCISͱCISͷؔ 18
NCISͱCISͷؔ CIS͕͍࣋ͬͯͨόΠΞε Λୈೋ߲ͰϞσϧ ͍ͯ͠ΔͱݟͳͤΔ 19
NCISͱCISͷؔ (ಛʹzero cappingͷ࣌) 20
NCISͱCISͷؔ (ಛʹzero cappingͷ࣌) — ͳΒۙతʹόΠΞ ε͕ͳ͘ͳΔ ! — ͷ ,
ʹର͢Δґଘ͕খ͍࣌͞ͳͲ 21
NCISͷόΠΞε 22
NCISͷόΠΞε — ͱcappingͷ༗ແʹ૬͕ؔ͋ΔͱόΠΞε͕େ͖͘ ͳΔ ! — ަབྷҼࢠϢʔβʔͷλΠϓͳͲ͕ߟ͑ΒΕΔ (Table 1) 23
NCISͷόΠΞε 24
จͷΞΠσΞ — ͷϞσϦϯάΛάϩʔόϧ㱺ϩʔΧϧʹ — ίϯςΩετ ʹରͯ͠ہॴతͳNCIS — ͱcappingͷ૬ؔΛݮΒ͢ — Piecewise
NCIS: ׂ͞ΕͨྖҬ͝ͱʹNCIS — Pointwise NCIS: ཁૉ͝ͱʹNCIS 25
Piecewise NCIS (PieceNCIS) ίϯςΩετͷू߹ ͷׂ Λߟ͑Δ 26
Piecewise NCIS (PieceNCIS) ׂ֤ʹରͯ͠NCIS 27
ׂͷྫ దͳؔ ΛఆΊͯ ֤ Ͱ ͷ ʹର͢Δґଘ͕খ͘͞ͳΔΑ͏ʹ 28
Pointwise NCIS (PointNCIS) ཁૉ୯ҐͰׂ͢Δ (i.e. ) ಛఆͷίϯςΩετʹର͢Δαϯϓϧ͘͝গͳ͍ͷ ͰૉʹNCISΛద༻Ͱ͖ͳ͍ 29
Pointwise NCIS (PointNCIS) — ΞΫγϣϯʹ͍ͭͯपลԽ͢Δ ͱਖ਼֬ʹٻΊΒΕΔ — ΞΫγϣϯͷ͕ଟ͍ͱܭࢉ͕ߴίετ ! —
ΛαϯϓϦϯάͰٻΊΔ 30
Midzuno-Sen method 1. Λαϯϓϧ 2. Λ ͔Β ͳͷ͕ಘΒΕΔ·Ͱαϯϓϧ 3. Λ
͔Βαϯϓϧ 4. Λฦ͢ ͜͏ͯ͠ಘΒΕΔΛ ͱॻ͘ 31
Pointwise NCIS (PointNCIS) — ͷ͏ͪ ͕ ͷσʔλແࢹͰ͖Δ — ใु͕εύʔεͳ࣌ʹޮతʹܭࢉͰ͖Δ !
32
࣮ݧ — ϓϩϓϥΠΤλϦͷσʔληοτ — 39छɺ߹ܭͰઍԯ݅ͷϩάσʔλ — ΫϦοΫϕʔεͷใु (εύʔε͔ͭࢄେ) — ରCIS,
NCIS, PieceNCIS, PointNCIS ( ) — IS, NISόϦΞϯε͕ߴ͗͢ΔͷͰআ֎ 33
ΦϯϥΠϯʗΦϑϥΠϯABςετͷ૬ؔ 34
ద߹ͱِӄੑ ʮ ͕ ΑΓΑ͍͔Ͳ͏͔ʯͷ2༧ଌͱͯ͠ݟΔ 35
࣮ݧ݁Ռͷ·ͱΊ — CIS૬͕ؔෛ — શମతʹΊͷਪఆ͕ग़͍ͯͨ (Figure 4) — CIS⇒NCISͰେ͖͘վળ —
NCIS⇒PointNCISͰِཅੑ͕͞ΒʹԼ͕Δ — ద߹NCISҎޙͦ͜·ͰΑ͘ͳΒͳ͍ — ࣮ߦʹ͓͍ͯਫ਼ʹ͓͍ͯPointNCIS͕Α͍ 36
Appendix — ͕খ͍͞ͱ ͕ cappingΛ͑Δ͜ͱ — Max cappingͰ ʹͳΔΑ͏ͳ ৽͍͠capping
͕ͱΕΔ (Lemma A.3) 37