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
Ken Wagatsuma
February 10, 2018
Technology
1
950
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
1
200
Kafka on Kubernetes with Strimzi
kenju
1
140
AWS DynamoDB Accelerator (DAX) 101
kenju
3
6.9k
Moden browser introduction
kenju
1
370
Cookpad summer internship 2019 - API
kenju
0
10k
Introduction to Design Patterns
kenju
0
69
GraphQL Asia 2019 "Re-architecture of a decade-old app with BFF/GraphQL"
kenju
0
8.8k
Introduction to TypeScript
kenju
0
690
Introduction to Programmatic Ad
kenju
0
230
Other Decks in Technology
See All in Technology
Azureの開発で辛いところ
re3turn
0
240
Alignment and Autonomy in Cybozu - 300人の開発組織でアラインメントと自律性を両立させるアジャイルな組織運営 / RSGT2025
ama_ch
1
2.4k
Formal Development of Operating Systems in Rust
riru
1
420
comilioとCloudflare、そして未来へと向けて
oliver_diary
6
450
re:Invent2024 KeynoteのAmazon Q Developer考察
yusukeshimizu
1
150
Copilotの力を実感!3ヶ月間の生成AI研修の試行錯誤&成功事例をご紹介。果たして得たものとは・・?
ktc_shiori
0
350
ゼロからわかる!!AWSの構成図を書いてみようワークショップ 問題&解答解説 #デッカイギ #羽田デッカイギおつ
_mossann_t
0
1.5k
AIアプリケーション開発でAzure AI Searchを使いこなすためには
isidaitc
1
120
GoogleのAIエージェント論 Authors: Julia Wiesinger, Patrick Marlow and Vladimir Vuskovic
customercloud
PRO
0
160
三菱電機で社内コミュニティを立ち上げた話
kurebayashi
1
360
RubyでKubernetesプログラミング
sat
PRO
4
160
生成AI × 旅行 LLMを活用した旅行プラン生成・チャットボット
kominet_ava
0
160
Featured
See All Featured
The Cult of Friendly URLs
andyhume
78
6.1k
The Success of Rails: Ensuring Growth for the Next 100 Years
eileencodes
44
7k
ReactJS: Keep Simple. Everything can be a component!
pedronauck
666
120k
Docker and Python
trallard
43
3.2k
How To Stay Up To Date on Web Technology
chriscoyier
790
250k
The Art of Programming - Codeland 2020
erikaheidi
53
13k
[RailsConf 2023 Opening Keynote] The Magic of Rails
eileencodes
28
9.2k
Building Flexible Design Systems
yeseniaperezcruz
328
38k
Practical Tips for Bootstrapping Information Extraction Pipelines
honnibal
PRO
10
870
Statistics for Hackers
jakevdp
797
220k
Building a Modern Day E-commerce SEO Strategy
aleyda
38
7k
Why Our Code Smells
bkeepers
PRO
335
57k
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