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
広告配信サーバーと広告配信比率最適化問題
Search
Sponsored
·
Your Podcast. Everywhere. Effortlessly.
Share. Educate. Inspire. Entertain. You do you. We'll handle the rest.
→
Ken Wagatsuma
February 10, 2018
Technology
1
1k
広告配信サーバーと広告配信比率最適化問題
Lightening Talk at
https://techconf.cookpad.com/2018/
Ken Wagatsuma
February 10, 2018
Tweet
Share
More Decks by Ken Wagatsuma
See All by Ken Wagatsuma
Pregel Graph Compute Engines - Supersteps Exampls
kenju
0
230
Kafka on Kubernetes with Strimzi
kenju
0
190
AWS DynamoDB Accelerator (DAX) 101
kenju
2
7.3k
Moden browser introduction
kenju
1
450
Cookpad summer internship 2019 - API
kenju
0
10k
Introduction to Design Patterns
kenju
0
110
GraphQL Asia 2019 "Re-architecture of a decade-old app with BFF/GraphQL"
kenju
0
9.1k
Introduction to TypeScript
kenju
0
760
Introduction to Programmatic Ad
kenju
0
280
Other Decks in Technology
See All in Technology
SREのプラクティスを用いた3領域同時 マネジメントへの挑戦 〜SRE・情シス・セキュリティを統合した チーム運営術〜
coconala_engineer
2
780
旅先で iPad + Neovim で iOS 開発・執筆した話
zozotech
PRO
0
100
SREが向き合う大規模リアーキテクチャ 〜信頼性とアジリティの両立〜
zepprix
0
480
プレビュー版のDevOpsエージェントを現段階で触ってみた
ad_motsu
1
100
10Xにおける品質保証活動の全体像と改善 #no_more_wait_for_test
nihonbuson
PRO
2
340
ランサムウェア対策としてのpnpm導入のススメ
ishikawa_satoru
0
230
Bill One急成長の舞台裏 開発組織が直面した失敗と教訓
sansantech
PRO
2
410
ClickHouseはどのように大規模データを活用したAIエージェントを全社展開しているのか
mikimatsumoto
0
270
顧客との商談議事録をみんなで読んで顧客解像度を上げよう
shibayu36
0
340
Exadata Fleet Update
oracle4engineer
PRO
0
1.1k
1,000 にも届く AWS Organizations 組織のポリシー運用をちゃんとしたい、という話
kazzpapa3
0
190
猫でもわかるKiro CLI(セキュリティ編)
kentapapa
0
130
Featured
See All Featured
The Power of CSS Pseudo Elements
geoffreycrofte
80
6.2k
Dominate Local Search Results - an insider guide to GBP, reviews, and Local SEO
greggifford
PRO
0
80
How To Stay Up To Date on Web Technology
chriscoyier
791
250k
The B2B funnel & how to create a winning content strategy
katarinadahlin
PRO
1
280
VelocityConf: Rendering Performance Case Studies
addyosmani
333
24k
How to Build an AI Search Optimization Roadmap - Criteria and Steps to Take #SEOIRL
aleyda
1
1.9k
Faster Mobile Websites
deanohume
310
31k
Highjacked: Video Game Concept Design
rkendrick25
PRO
1
290
Principles of Awesome APIs and How to Build Them.
keavy
128
17k
Templates, Plugins, & Blocks: Oh My! Creating the theme that thinks of everything
marktimemedia
31
2.7k
[RailsConf 2023 Opening Keynote] The Magic of Rails
eileencodes
31
9.9k
Building the Perfect Custom Keyboard
takai
2
690
Transcript
ࠂ৴αʔόʔͱ ࠂ৴ൺ࠷దԽ ϝσΟΞϓϩμΫτ։ൃ෦ ,FOKV8BHBUTVNB
8IP Kenju Wagatsuma (github.com/kenju) • ϝσΟΞϓϩμΫτ։ൃ෦ • αʔόʔαΠυΤϯδχΞ • ͖ͳͷɿRuby,
ίʔώʔ, ϩδΧϧΫοΩϯά • ݏ͍ͳͷɿ1ϲ݄લʹॻ͍ͨࣗͷίʔυ
ϝσΟΞϓϩμΫτ։ൃ෦ ୲αʔϏεɿ ࠂ৴, storeTV, cookpadTV, OEM, ͦͷଞଟ ࢀߟɿ ։ൃऀϒϩάʰΫοΫύουͷࠂΤϯδχΞԿΛ ͍ͬͯΔͷ͔ʱ
ຊ͍ͨ͜͠ͱɻ ϝσΟΞϓϩμΫτ։ൃ෦Ͱ ͲΜͳϓϩδΣΫτΛ͍ͬͯΔͷ͔ʁ
νʔϜʹೖͬͯϲ݄ޙʹऔΓΜͩϓϩδΣΫτ ΫοΫύουͷࠂ৴αʔόʔʹ͓͚Δ ࠂ৴ൺͷࣗಈ࠷దԽϓϩδΣΫτɻ
ݫ͍͠εέδϡʔϧ • ϝσΟΞϓϩμΫτ։ൃ෦δϣΠϯ - 10݄த० • ͓खฒΈഈݟϓϩδΣΫτ - ~11݄த० •
৴࠷దԽτϥΠΞϧ - 12/4(݄) 10:00 - 12/11(݄) 10:00 ???
ղܾ͍ͨ͠՝ • ʑͷखӡ༻ʹΑΔνϡʔχϯά͕ඞཁ - => ࡞ۀ͕ൃੜ • ӡ༻ऀͷܦݧͱצʹཔͬͨνϡʔχϯά - =>
ҟಈ࣌ಋೖ࣌ͷίετ͕ߴա͗ • ࠷దͳࡏݿൺΛࣗಈͰௐͰ͖ͳ͍ - => ࠂܝग़ͷػձଛࣦ
Ͳ͏ղܾ͢Δ͔ • ࡏݿׂྔͱ࣮͔Β࠷దͳ৴ൺͷิ ਖ਼Λߦ͏ - ΠϯϓϨογϣϯϕʔε͔ΒΫϦοΫϕʔεͷ৴ - ΫϦοΫ༧ଌΛར༻ͨ͠ൺͷࣗಈ࠷దԽ - ϦΞϧλΠϜूܭσʔλΛ׆༻ͨ͠ΞʔΩςΫνϟ
‣ Lambda Architecture ʹ͓͚Δ Speed Layer
l4QFFE-BZFSzPO"84 • Kinesis, DynamoDB, Lambda Λ׆༻ͨ͠ Speed Layer (from Lambda
Architecture) • طଘͷετϦʔϜʹɺΫ ϦοΫܭࢉϨΠϠʔΛ Ճ͚ͨͩ͠ = ઌਓͷݞ ʹΔ
ৄ͍ͪ͜͠Β ࢀߟɿ ʰCookpad Tech Kitchen #9 ʙ1ߦͷϩάͷ͜͏ ଆʙ Λ։࠵͠·ͨ͠ʂʱ
ΫϦοΫ༧ଌ͍͠ʂʂʂ • ޯϒʔεςΟϯάܾఆʢGBDTʣΛ༻͍ͨࠂ͝ͱͷΫϦοΫ༧ଌ - Facebook https://code.facebook.com/posts/975025089299409/evaluating-boosted-decision-trees-for-billions-of-users - SmartNews https://speakerdeck.com/komiya_atsushi/gbdt-niyorukuritukulu-yu-ce-wogao-su-hua-sitai-number-oresikanaito-vol-dot-4 •
ଟόϯσΟοτͷҰछͰ͋ΔMortal Multi-Armed BanditsͷԠ༻ - Voyage Group http://techlog.voyagegroup.com/entry/2015/04/03/114547ɹ • Neural Networkͷ૯߹֨ಆٕʢ͕͢͞Googleʣ - Google http://www.eecs.tufts.edu/~dsculley/papers/ad-click-prediction.pdfɹ • ৴པͱ࣮ͷϩδεςΟοΫճؼʢୠܻ͕͠ԯϨϕϧʣ - Criteo http://olivier.chapelle.cc/pub/ngdstone.pdfɹ
ؒʹ߹Θͳ͍ʂ • τϥΠΞϧͳΜͱͯ͠ʹ࣮ࢪ͍ͨ͠ - վળͷαΠΫϧΛճͨ͢Ί • QCDͰݴ͏ͳΒɺDelivery, QualityΛ༏ઌ - ͳΜͱͯؒ͠ʹ߹Θ͍ͤͨʂ
• ࠷ॳ͔Βᘳͳਫ਼༧ଌ·ͣෆՄೳ - ػցֶशͰղܾ͠ͳͯ͘Α͍͔·ͣߟ͑Δ - ࢀߟɿʰࣄͰ͡ΊΔػցֶशʱ
ҠಈฏۉԞ͕ਂ͍ • SMA (Simple Moving Average) = ۙ N ݸͷॏΈ͚ͷͳ͍୯७ͳฏۉ
• WMA (Weighted Moving Average) = ΑΓ࠷ۙͷσʔλʹॏΈ͚ • EWMA (Exponentially Weighted Moving Average) = ࢦؔతʹॏΈ͚ • MMA (Modified Moving Average) = EWMAͷѥछ ଞʹTriangle MA, Sine Weighted MA, KZ Filtering,...etc ࢀߟɿhttps://en.wikipedia.org/wiki/Moving_average#Simple_moving_averag
աڈϩάΛݩʹΞϧΰϦζϜͷਫ਼Λੳ • Jupyter Notebook / Python - ࢀߟɿ։ൃऀϒϩάʰRailsΤϯ δχΞʹཱͭJupyter Notebook
ͱiRubyʱ • ൺֱͨ͠ΞϧΰϦζϜ - Total Average - Cumulative Average - Simple Moving Average (3 Hours) - Simple Moving Average (6 Hours)
τϥΠΞϧ݁Ռ • ิਖ਼ͷϩδοΫʹ՝ ͕ݟ͔ͭͬͨ ͷɺτϥΠΞϧͱ͠ ͯޭ
ظతνϡʔχϯά • Speed Layer ͷ࠶ઃܭɾຏ͖ࠐΈ - ετϦʔϜॲཧʹԊͬͨσʔλͷྲྀΕ • ෛ࠴ =
ະୡ ΛՃຯͨ͠ϩδοΫ - ୈҰ࣍τϥΠΞϧΛ͍ͬͯͳ͔ͬͨΒݟ͑ͳ͔ͬͨ՝ • ҠಈฏۉΞϧΰϦζϜͷվળ - Batch LayerͰΦϑϥΠϯͰܭࢉ&࠷ਫ਼͕ྑ͍ͷΛબ - Gem࡞ͬͨ https://github.com/kenju/moving_avg-ruby
தظͰ͍͖ͬͯ • ΫϦοΫ༧ଌਫ਼ͷߋͳΔ্ˍ৽نࠂ։ൃ - ػցֶशϨΠϠʔͷຊ൪ಋೖ • Lambda Architectureͷຏ͖ࠐΈ - ࢀߟɿ։ൃऀϒϩάʰαʔόʔϨεͳόοΫΞοϓγεςϜ
Λ AWS SAM Λ༻͍ͯγϡοͱߏங͢Δʱ • ࠂ৴αʔόʔࣗମͷѹతվળ - ։ൃج൫ͷڥඋ - ύϑΥʔϚϯε࠷దԽɺϨΨγʔίʔυͷվળ
ຖͷྉཧΛָ͠Έʹ͢Δ 5IBOLZPV