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
論文紹介 Balancing Relevance and Discovery to Inspi...
Search
Takashi Nishibayashi
October 17, 2020
Research
0
740
論文紹介 Balancing Relevance and Discovery to Inspire Customers in the IKEA App
RecSys2020論文読み会の発表資料です
https://connpass.com/event/189192/
Takashi Nishibayashi
October 17, 2020
Tweet
Share
More Decks by Takashi Nishibayashi
See All by Takashi Nishibayashi
診断前の病歴テキストを対象としたLLMによるエンティティリンキング精度検証
hagino3000
1
140
論文紹介 Improving Medical Reasoning through Retrieval and Self-Reflection with Retrieval-Augmented Large Language Models
hagino3000
0
870
論文紹介 Audience Size Forecasting Fast and Smart Budget Planning for Media Buyers
hagino3000
0
240
論文紹介 Towards a Fair Marketplace: Counterfactual Evaluation of the trade-off between Relevance, Fairness & Satisfaction in Recommendation Systems
hagino3000
1
630
論文紹介 Budget Management Strategies in Repeated Auctions (公開版)
hagino3000
2
290
論文紹介 A Request-level Guaranteed Delivery Advertising Planning: Forecasting and Allocation
hagino3000
1
120
論文紹介 Online Experimentation with Surrogate Metrics Guidelines and a Case Study
hagino3000
1
360
論文紹介 Bidding Machine: Learning to Bid for Directly Optimizing Profits in Display Advertising
hagino3000
1
210
不確実性と上手く付き合う意思決定の手法
hagino3000
19
15k
Other Decks in Research
See All in Research
AWSで実現した大規模日本語VLM学習用データセット "MOMIJI" 構築パイプライン/buiding-momiji
studio_graph
2
620
RHO-1: Not All Tokens Are What You Need
sansan_randd
1
180
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
560
2025/7/5 応用音響研究会招待講演@北海道大学
takuma_okamoto
1
210
SSII2025 [TS2] リモートセンシング画像処理の最前線
ssii
PRO
7
3.1k
言語モデルの地図:確率分布と情報幾何による類似性の可視化
shimosan
5
1.6k
生成的推薦の人気バイアスの分析:暗記の観点から / JSAI2025
upura
0
280
EOGS: Gaussian Splatting for Efficient Satellite Image Photogrammetry
satai
4
620
AlphaEarth Foundations: An embedding field model for accurate and efficient global mapping from sparse label data
satai
3
300
Submeter-level land cover mapping of Japan
satai
3
380
長期・短期メモリを活用したエージェントの個別最適化
isidaitc
0
140
Vision and LanguageからのEmbodied AIとAI for Science
yushiku
PRO
1
550
Featured
See All Featured
How To Stay Up To Date on Web Technology
chriscoyier
791
250k
BBQ
matthewcrist
89
9.8k
Distributed Sagas: A Protocol for Coordinating Microservices
caitiem20
333
22k
What’s in a name? Adding method to the madness
productmarketing
PRO
23
3.7k
Building a Scalable Design System with Sketch
lauravandoore
462
33k
Building Better People: How to give real-time feedback that sticks.
wjessup
368
20k
GitHub's CSS Performance
jonrohan
1032
460k
Balancing Empowerment & Direction
lara
4
670
Music & Morning Musume
bryan
46
6.8k
Practical Orchestrator
shlominoach
190
11k
GraphQLとの向き合い方2022年版
quramy
49
14k
Helping Users Find Their Own Way: Creating Modern Search Experiences
danielanewman
30
2.9k
Transcript
จհ #BMBODJOH3FMFWBODFBOE%JTDPWFSZ UP*OTQJSF$VTUPNFSTJOUIF*,&""QQ 3FD4ZTจಡΈձ ྛ !IBHJOP
"CPVUNF ✦ 4PGUXBSF&OHJOFFSBU70:"(&(3061 ✦ ࠂ৴ϓϩμΫτͷ։ൃΛ͍ͯ͠·͢ ✦ 3FD4ZTॳࢀՃ ✦ 5XJUUFS!IBHJOP
հ͢Δจ
༰ ✦ *,&"ΞϓϦͷ*OTQJSBUJPOBM'FFEʹ͓͚ΔϦίϝϯυํࡦʹ#BOEJU 'FFECBDLϩάͬͨϙϦγʔֶशΞϓϩʔνΛద༻ͨ͠ ✦ Ϣʔβʔ͕ΠϯεϐϨʔγϣϯΛಘΒΕΔ༷ʹDPVOUFSGBDUVBMSJTL NJOJNJ[BUJPOQSJODJQMF<>ʹج͍ͮͯํࡦΛֶशͨ͠ ✦ "#ςετͷ݁ՌɺڠௐϑΟϧλϦϯάʹΑΔํࡦͱൺֱͯ͠ΫϦοΫ ্͕ঢͨ͠
✦ ϖʔύʔʹଛࣦ࣮ؔݧ݁Ռ΄ͱΜͲॻ͍ͯͳ͔ͬͨͷͰɺ͜ͷൃ දޱ಄ൃද༰Λͬͯิ͍ͯ͠·͢
*OTQJSBUJPOBM'FFE ✦ Ϣʔβʔ༷ʑͳ෦ͷλΠϓʹ͋ΘͤͯՈ۩͕ ஔ͞ΕͨΠϝʔδ͕ӾཡͰ͖Δ ✦ ͜ͷϑΟʔυϢʔβʔʹؔ࿈͕͋ΓΠϯεϐ ϨʔγϣϯΛ༩͑Δͷʹ͍ͨ͠ɻڻ͖ͷཁૉ͕ ٻΊΒΕΔɻ 3FMFWBODFBOE%JTDPWFSZUP*OTQJSF
✦ ؔ࿈ੑʹಛԽͨ͠Ϧίϝϯυػೳطʹ͋Δ
$POUFYUVBMCBOEJUTXJUICBUDIMFBSOJOHGSPN MPHHFECBOEJUGFFECBDL ✦ Ͳͷը૾Λදࣔ͢Δ͔ ✦ $POUFYUVBM#BOEJUTͰܾΊΔ ✦ $POUFYUVBM#BOEJUTͷߦಈબϙϦγʔͷֶश ✦ όονͰΔ
㱠ΦϯϥΠϯֶश ✦ #BOEJU'FFECBDLϩάΛͬͯ܇࿅ ✦ CBTFEPOUIFQSJODJQMFPGDPVOUFSGBDUVBMSJTLNJOJNJ[BUJPO<> ✦ $PVOUFSGBDUVBM-FBSOJOH
✦ ߦಈ ✦ ը૾Λબͯ͠දࣔ͢Δࣄ ✦ ใु ✦ දࣔͨ͠ը૾͕ΫϦοΫ͞ΕΔ͔Ͳ͏͔㱨\ ^ ✦
ίϯςΩετ ✦ ΞϓϦ্ͷϢʔβʔࣗͷաڈͷ;Δ·͍ *OTQJSBUJPOBM'FFEͷ#BOEJUઃఆ
$POUFYUVBMCBOEJUT ܁Γฦ͠ҙࢥܾఆʹ͓͍ͯྦྷੵใुͷ࠷େԽΛૂ͏ํࡦͷͳ͔Ͱ ϥϯυຖͷίϯςΩετใΛར༻ͯ͠ٻΊͨείΞʹैͬͯߦಈΛબ͢ Δͷɻ؍ଌͨ͠ใुΛͬͯείΞϦϯάϞσϧΛߋ৽͍ͯ͘͠ɻ είΞϦϯάϞσϧʹઢܗϞσϧΛ࠾༻ͨ͠-JO6$#<>ͳͲɺ༷ʑͳํࡦ͕ఏҊ͞Ε͍ͯΔ
ࢀߟ-JO6$#<> ϥϯυUʹ͓͚ΔߦಈBͷ είΞใुͷظ ඪ४ภࠩºЋ είΞ͕࠷େͷߦಈΛ࣮ߦ ύϥϝʔλߋ৽
#BUDIMFBSOJOHGSPNMPHHFECBOEJUGFFECBDL ✦ CBOEJUGFFECBDLϩάΛֶͬͨश ✦ աڈͷߦಈબϙϦγʔʹΑΔόΠΞεͷิਖ਼͕ඞཁ ✦ ਪનγεςϜͷϩάجຊతʹCBOEJUGFFECBDL ✦ ΦϯϥΠϯֶशͰͳ͍ཧ༝ಛʹઆ໌͕ແ͔͕ͬͨ ✦
ϦΫΤετྔ͕ଟ͍αʔϏεͰόϯσΟοτΞϧΰϦζϜΛ͏߹ จͷखଓ͖௨ΓʹύϥϝʔλΛஞ࣍ߋ৽͢Δέʔεগͳ͍ͱࢥ͏ ✦ ΦϯϥΠϯֶशӡ༻͕େม
ิ#BOEJU'FFECBDLϩάΛֶͬͨश ✦ #BOEJU'FFECBDLϩάΛར༻ͨ͠৽͍͠ߦಈબϙϦγʔͷੑೳධՁΛߦͳ͏ ख๏ଘࡏ͢Δ ˠ0⒎1PMJDZ&WBMVBUJPO *OWFSTF1SPQFOTJUZ4DPSJOH %PVCMZ3PCVTU ʜ ✦
ੑೳධՁ͕࠷େʹͳΔ༷ʹߦಈબϙϦγʔΛֶश͢Εྑ͍ ✦ ߦಈΛBɺίϯςΩετΛYɺใुΛSͱͯ͠*14ͰධՁ͢Δ߹ КOFXͷ*14$PVOUFSGBDUVBM&TUJNBUPS
ߦಈબϙϦγʔͷֶश ϙϦγʔК͕ίϯςΩετYʹରͯ͠ߦಈZΛબͿ֬ είΞ ΛК ZcY ใुΛЎͱ͓͘ɻաڈͷϙϦγʔКͷείΞͱ؍ଌͨ͠ใुΛར༻ͯ͠ɺ৽ͨͳ ϙϦγʔКВΛֶश͢Δ ޱ಄ൃදεϥΠυ͔Βഈआ ͜ͷ··Ͱࢄͷ͕ग़ΔͷͰɺ<>Λࢀߟʹ͍͔ͭ͘ͷΛऔΓ͍Ε͍ͯΔͱͷઆ
໌͕͋ͬͨ
ଛࣦؔͷײతͳղऍ ใु͕ಘΒΕͨߦಈͰಛʹաڈͷϙϦγʔͷείΞ͕͍ߦಈͷείΞ্͕ ͕Εϩε͕Լ͕Δɻͭ·Γ͋·Γબ͠ͳ͔͕ͬͨΫϦοΫ͕ಘΒΕͨߦಈΛଟ͘ બͿ༷ʹֶश͢Δɻ ޱ಄ൃදεϥΠυ͔Βഈआ
ଛࣦؔͲ͔͜Βདྷͨͷ͔ ޱ಄ൃදεϥΠυͷࣜ3FGFSFODFʹ͋Δ 4XBNJOBUIBOΒ$PVOUFSGBDUVBMSJTL NJOJNJ[BUJPO-FBSOJOHGSPNMPHHFECBOEJU GFFECBDLz *$.- <>ʹ͓͚ΔఏҊख๏ ͷಋग़ͷং൫ʹ͋Δ*14ϕʔεͷࣜɻ ͳͷͰ࣮ࡍʹ͍ͬͯΔ
ͷ<>ͷఏҊख๏ͷࣜ ͩͱࢥΘΕΔ
$PVOUFSGBDUVBMSJTLNJOJNJ[BUJPO-FBSOJOHGSPNMPHHFE CBOEJUGFFECBDL *$.- <> ✦ *14ͷࢄ༝དྷͷΤϥʔΛόϯυ͢ΔVOCJBTFEFTUJNBUPSͰ͋Δ $3.$PVOUFSGBDUVBM3JTL.JOJNJ[BUJPOΛఏҊɺࢄΛਖ਼ଇԽ߲ʹ ✦ $3.Λֶश͢Δ܇࿅ΞϧΰϦζϜ10&.ͷఏҊ
ิ%PVCMZSPCVTUNFUIPEGPSDPVOUFSGBDUVBM MFBSOJOH Yuan, Bowen, et al. "Improving ad click prediction
by considering non-displayed events." Proceedings of the 28th ACM International Conference on Information and Knowledge Management. 2019.
ΦϯϥΠϯධՁ ✦ ධՁࢦඪ$53 ✦ ͷ্͕֬ೝͰ͖ͨ ✦ ڠௐϑΟϧλϦϯάϕʔεͷख๏ͱൺֱ ✦ ڠௐϑΟϧλϦϯά3FMFWBODFॏࢹ ✦
ॳͷతୡͰ͖ͨ ✦ આ໌ແ͔͕ͬͨʮࠓ·Ͱʹਪન͠ͳ͔ͬͨΞΠςϜΛଟ͘ਪન͢ΔࣄͰΫ ϦοΫ͕૿͑ͨʯˠ*OTQJSBUJPOΛ༩͑Δࣄ͕Ͱ͖ͨͱղऍͰ͖ͦ͏ ✦ ΫϦοΫ͕ݮ͍ͬͯͳ͍ˠϏδωεࢦඪΛᆝଛ͍ͯ͠ͳ͍
ͷ࣭ٙԠͷൈਮ 2%PVCMZ3PCVTUͰͳ͘*14ʹͨ͠ͷԿނ ಛʹΦϑϥΠϯධՁͰࢄ͕ʹͳΒͳ͔͔ͬͨ "%PVCMZ3PCVTUߟ͑ͳ͔ͬͨɻ͔͠͠ࢄΛ͑ΔͨΊʹεί ΞͷΫϦοϐϯάΛߦͳͬͨ 2"#ςετΛि͚ؒͩΒͤͨ͜ͱͰɺϢʔβʔ͕׳Ε͠Μͩ " ͱҧͬ
͍ͯͨͨΊ # ͷ$53͕ߴ͘ͳͬͨՄೳੑ͋Δ͔ "ͦͷޙҰ؏ͯ͠ߴ͍$53Λ͍ࣔͯ͠Δ͔ΒɺͦΕແ͍ͱߟ͍͑ͯΔ 2୳ࡧΛߦͳ͏ࣄͰΫϦοΫͷݮগΈΒΕͳ͔͔ͬͨ "શ͘ٯͰ૿Ճͨ͠
ࢀߟจݙ <>5ÓUI #BMÂ[T 4BOEIZB4BDIJEBOBOEBO BOE&NJM4+SHFOTFO#BMBODJOH3FMFWBODFBOE %JTDPWFSZUP*OTQJSF$VTUPNFSTJOUIF*,&""QQ'PVSUFFOUI"$.$POGFSFODFPO 3FDPNNFOEFS4ZTUFNT <>-J -JIPOH FUBM"DPOUFYUVBMCBOEJUBQQSPBDIUPQFSTPOBMJ[FEOFXTBSUJDMF
SFDPNNFOEBUJPO1SPDFFEJOHTPGUIFUIJOUFSOBUJPOBMDPOGFSFODFPO8PSMEXJEFXFC <>4XBNJOBUIBO "EJUI BOE5IPSTUFO+PBDIJNT$PVOUFSGBDUVBMSJTLNJOJNJ[BUJPO-FBSOJOH GSPNMPHHFECBOEJUGFFECBDL*OUFSOBUJPOBM$POGFSFODFPO.BDIJOF-FBSOJOH <>:VBO #PXFO FUBM*NQSPWJOHBEDMJDLQSFEJDUJPOCZDPOTJEFSJOHOPOEJTQMBZFE FWFOUT1SPDFFEJOHTPGUIFUI"$.*OUFSOBUJPOBM$POGFSFODFPO*OGPSNBUJPOBOE ,OPXMFEHF.BOBHFNFOU