$30 off During Our Annual Pro Sale. View Details »
Speaker Deck
Features
Speaker Deck
PRO
Sign in
Sign up for free
Search
Search
Offline A/B testing for Recommender Systems
Search
alpicola
November 20, 2018
Technology
0
2.1k
Offline A/B testing for Recommender Systems
alpicola
November 20, 2018
Tweet
Share
More Decks by alpicola
See All by alpicola
商品レコメンドでのexplicit negative feedbackの活用
alpicola
2
880
Recommending What Video to Watch Next: A Multitask Ranking System
alpicola
1
900
Kibanaを用いたアクセスログ調査と解析 / Access Log Analysis Using Kibana
alpicola
0
980
Other Decks in Technology
See All in Technology
小さな判断で育つ、大きな意思決定力 / 20251204 Takahiro Kinjo
shift_evolve
PRO
1
300
All About Sansan – for New Global Engineers
sansan33
PRO
1
1.3k
M5UnifiedとPicoRubyで楽しむM5シリーズ
kishima
0
110
一億総業務改善を支える社内AIエージェント基盤の要諦
yukukotani
8
2.8k
AI時代の開発フローとともに気を付けたいこと
kkamegawa
0
160
Introduction to Bill One Development Engineer
sansan33
PRO
0
330
“決まらない”NSM設計への処方箋 〜ビットキーにおける現実的な指標デザイン事例〜 / A Prescription for "Stuck" NSM Design: Bitkey’s Practical Case Study
bitkey
PRO
1
340
手動から自動へ、そしてその先へ
moritamasami
0
180
pmconf2025 - 他社事例を"自社仕様化"する技術_iRAFT法
daichi_yamashita
0
490
日本Rubyの会の構造と実行とあと何か / hokurikurk01
takahashim
2
410
mablでリグレッションテストをデイリー実行するまで #mablExperience
bengo4com
0
470
freeeにおけるファンクションを超えた一気通貫でのAI活用
jaxx2104
3
600
Featured
See All Featured
How STYLIGHT went responsive
nonsquared
100
5.9k
The Psychology of Web Performance [Beyond Tellerrand 2023]
tammyeverts
49
3.2k
Designing Experiences People Love
moore
142
24k
XXLCSS - How to scale CSS and keep your sanity
sugarenia
249
1.3M
Product Roadmaps are Hard
iamctodd
PRO
55
12k
Building Applications with DynamoDB
mza
96
6.8k
CSS Pre-Processors: Stylus, Less & Sass
bermonpainter
359
30k
Fashionably flexible responsive web design (full day workshop)
malarkey
407
66k
Building Better People: How to give real-time feedback that sticks.
wjessup
370
20k
A Modern Web Designer's Workflow
chriscoyier
697
190k
BBQ
matthewcrist
89
9.9k
Fight the Zombie Pattern Library - RWD Summit 2016
marcelosomers
234
17k
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