Upgrade to Pro
— share decks privately, control downloads, hide ads and more …
Speaker Deck
Sign up for free
Menu
Search
Features
All features
Private URLs
Password Protection
Custom URLS
Scheduled publishing
Remove Branding
Restrict embedding
Deck Collections
Notes
Features
All features
Private URLs
Password Protection
Custom URLS
Scheduled publishing
Remove Branding
Restrict embedding
Deck Collections
Notes
Explore
Featured decks
Featured speakers
Programming
Technology
Storyboards
Explore
Featured decks
Featured speakers
Programming
Technology
Storyboards
Pricing
Search
Sign in
Sign up for free
20180414_WSDM2018_reading_YoheiKIKUTA
Search
Sponsored
·
Your Podcast. Everywhere. Effortlessly.
Share. Educate. Inspire. Entertain. You do you. We'll handle the rest.
→
yoppe
April 12, 2018
Science
760
0
Share
Embed
Copy iframe code
Copy JS code
Copy link
Start on current slide
20180414_WSDM2018_reading_YoheiKIKUTA
HP:
https://atnd.org/events/95510
yoppe
April 12, 2018
More Decks by yoppe
See All by yoppe
20211023_recsys2021_paper_reading_YoheiKikuta
diracdiego
1
530
20201121_oldpaperreading_computing_machinery_and_intelligence
diracdiego
0
200
20200906_ACL2020_metric_for_ordinal_classification_YoheiKikuta
diracdiego
1
1.4k
20191102_ACL2019_adversarial_examples_in_NLP_YoheiKIKUTA
diracdiego
2
1.5k
20190223_nlpaperchallenge_CV_4.3to5.5
diracdiego
2
870
20180701_CVPR2018_reading_YoheiKIKUTA
diracdiego
3
1.3k
20180306_NIPS2017_DeepLearning
diracdiego
4
6k
20180215_MLKitchen7_YoheiKIKUTA
diracdiego
0
500
20180210_Cookpad_TechConf2018_YoheiKIKUTA
diracdiego
5
1.3k
Other Decks in Science
See All in Science
(CVPR2026) Back to Basics: Let Denoising Generative Models Denoise
shumpei777
0
440
O(log n)-Approximation Algorithms for Bipartiteness Ratio
tasusu
0
290
知能とはなにか -ヒトとAIのあいだ-
tagtag
PRO
0
250
CVPR2026_VGGTとその仲間たち
mickey_0226
0
1.3k
機械学習 - SVM
trycycle
PRO
2
1.3k
データベース06: SQL (3/3) 副問い合わせ
trycycle
PRO
1
1.2k
AlgorithAlgorihms for Decision Making
mickey_kubo
0
180
From Prediction to Understanding: Causal Discovery for Data Science and AI Applications
sshimizu2006
0
340
「念のためのログ保存」を組織全体でやめるためのポリシーと仕組み作り
i2tsuki
4
430
「表象」を再定義するー潜在表現解析の落とし穴
ykamit
0
1.1k
Understanding CVP Waveforms: Interpretation and Clinical Implications in Anesthesiology
taka88
0
1k
[Relay webinaire eBIS actu n°16] Amélioration de la survie des JB avec la génétique - Introduction
institutdelelevage
PRO
0
430
Featured
See All Featured
svc-hook: hooking system calls on ARM64 by binary rewriting
retrage
2
660
Designing Powerful Visuals for Engaging Learning
tmiket
1
620
Site-Speed That Sticks
csswizardry
13
1.6k
The Illustrated Guide to Node.js - THAT Conference 2024
reverentgeek
1
590
What the history of the web can teach us about the future of AI
inesmontani
PRO
2
770
Believing is Seeing
oripsolob
1
260
Let's Do A Bunch of Simple Stuff to Make Websites Faster
chriscoyier
508
140k
Mozcon NYC 2025: Stop Losing SEO Traffic
samtorres
1
590
How to Think Like a Performance Engineer
csswizardry
28
2.9k
What Being in a Rock Band Can Teach Us About Real World SEO
427marketing
0
1.1k
Balancing Empowerment & Direction
lara
6
1.3k
Heart Work Chapter 1 - Part 1
lfama
PRO
10
37k
Transcript
Why People Search for Images using Web Search Engines WSDM
2018 จಡΈձ 20180414 ٠ా ངฏ (@yohei_kikuta) Event URL: https://atnd.org/events/95510, paper: https://arxiv.org/abs/1711.09559
·ͱΊ 1. text base ͷΣϒը૾ݕࡧͷҙਤྨՄೳ͔ʁ → YES. 3ͭʹྨ: Entertain, Explore/Learn,
Locate/Acquire 2. औಘՄೳͳಛྔ͔ΒҙਤΛผͰ͖Δ͔ʁ → YES. ཹ࣌ؒϚεδΣενϟ 3. ηογϣϯॳظͰݕࡧҙਤΛ༧ଌͰ͖Δ͔ → MAYBE. ಛྔΛͬͯϞσϧΛ࡞ͯ͠Ұఆͷੑೳ 2
എܠ 3
ݕࡧͷҙਤΛΓ͍ͨ Ϣʔβͷݕࡧߦಈͷཪʹ͋ΔҙਤΛΔ͜ͱॏཁ → Ϣʔβͷຬ্ʢsuggestion, recommendation, ...ʣ Σϒݕࡧͷݚڀͳ͞Ε͖͕ͯͨɺը૾ݕࡧʹؔͯ͠ݶఆత → ΫΤϦϕʔε →
͔͠͠ը૾ݕࡧͷΫΤϦ͘ͳΓ͕ͪͰෆ࣮֬ੑ͕େ͖͍ ຊจͰηογϣϯใΛѻͬͯը૾ݕࡧͷҙਤΛݚڀ 4
ຊจʹ͓͚ΔϦαʔνΫΤενϣϯ 1. text base ͷΣϒը૾ݕࡧͷҙਤྨՄೳ͔ʁ 2. औಘՄೳͳಛྔ͔ΒҙਤΛผͰ͖Δ͔ʁ 3. ηογϣϯॳظͰݕࡧҙਤΛ༧ଌͰ͖Δ͔ 5
ઌߦݚڀ 6
Σϒݕࡧʹ͓͚Δҙਤͷ taxonomy A taxonomy of web search (2002) ͰҙਤΛ3ͭʹྨ 1.
Navigational: ಛఆͷαΠτ౸ୡ 2. Informational: ใͷऔಘ 3. Transactional: ΣϒΛഔհͱͨ͠׆ಈ Ref: https://dl.acm.org/citation.cfm?id=792552 7
Σϒݕࡧʹ͓͚Δҙਤͷ taxonomy Task Behaviors During Web Search: The Difficulty of
Assigning Labels (2009) ͰݕࡧλεΫΛ7ͭʹྨ » Navigate, Find-Simple, Find-Complex, Locate/Acquire, Explore/Learn, Play, Meta ຊจ͜ͷઌߦݚڀΛ౿ऻͭͭ͠ը૾ݕࡧʹൃలͤͨ͞ͷɺͱ͍͏ ৭߹͍͕ڧ͍ Ref: http://ieeexplore.ieee.org/document/4755491/ 8
ը૾ݕࡧͷҙਤΛྨ 9
Ξϓϩʔν σʔλΛूΊͯͦΕΛجʹ3ਓͷΣϒݚڀऀ͕ྨ » ϢʔβͷΞϯέʔτσʔλ » ੑผใͳͲΛऔಘ » ࠷ۙͷݕࡧʹؔ͢ΔৄࡉʢಈػͳͲʣɺ༻ͨ͠ΫΤϦ » దͳճΛͨ͠211ਓ͕ର
10
Ξϓϩʔν σʔλΛूΊͯͦΕΛجʹ3ਓͷΣϒݚڀऀ͕ྨ » ϩάσʔλ » https://www.sogou.com/ ͷϩάσʔλ » 30Ҏʹ࿈ଓతͳΫΤϦΛ༩͍͑ͯΔ475ηογϣϯʢআ͘Ξμ ϧτʣ
11
Ξϓϩʔν σʔλΛूΊͯͦΕΛجʹ3ਓͷΣϒݚڀऀ͕ྨ » ϩάσʔλʢlength ΫΤϦʣ Ref: https://arxiv.org/abs/1711.09559 12
࡞ͨ͠அج४ 1. Ϣʔβͷݕࡧߦಈ໌֬ͳతʹґΔͷ͔ʁ 2. ޙͷར༻ͷͨΊʹը૾Λμϯϩʔυ͢Δඞཁ͕͋Δ͔ʁ 13
3ͭͷݕࡧҙਤ 1. Explore/Learn (1-yes, 2-no) ྫʣΰϦϥͱϘϊϘͷݟͨͷҧ͍ΛνΣοΫ 2. Locate/Acquire (1-yes, 2-yes)
ྫʣϨϙʔτ࡞Ͱ͏ΰϦϥͷը૾Λ୳ͯ͠μϯϩʔυ 3. Entertain (1-no, 2-yes or no) ྫʣΰϦϥͷ໘നը૾ΛோΊΔ 14
3ͭͷݕࡧҙਤʢྫʣ Ref: https://arxiv.org/abs/1711.09559 15
ଥੑͷݕূʢ3ਓͷେֶӃੜʹΑΔҙਤྨʣ » ϢʔβͷΞϯέʔτσʔλ Fleiss' kappa: 0.673 Explore/Learn: 27%, Locate/Acquire: 66%,
Entertain: 7% » ϩάσʔλʢΫΤϦͷΈΛ༻ʣ Fleiss' kappa: 0.375 Explore/Learn: 56%, Locate/Acquire: 39%, Entertain: 5% ͏·͚͘Εͦ͏͕ͩΫΤϦͷΈͰҙਤΛΉͷ͍͠ 16
औಘՄೳͳಛྔͰҙਤΛผ 17
35ਓͷֶ෦ੜʹΑΔ12ݸͷը૾ݕࡧλεΫ ྫʣPCͷഎܠΛ੨ۭͱͷը૾ʹมߋʢLocate/Acquireʣ ͦͷࡍʹҎԼͷಛྔΛऔಘ Ref: https://arxiv.org/abs/1711.09559 18
ҙਤʹΑͬͯ༗ҙͳ͕ࠩग़ΔͷͰผՄೳ ఀཹ࣌ؒ E/L ͕ଟ͍ɺϚεΫϦοΫ E/L < L/A < EɺͳͲ ʢৄࡉจΛࢀরʣ
Ref: https://arxiv.org/abs/1711.09559 19
ηογϣϯॳظͰͷҙਤͷ༧ଌ 20
ઃఆ ηογϣϯॳظͱʮ࠷ॳͷϚεεΫϩʔϧ͕͋Δ·Ͱʯ ༧ଌͰ͏ feature ͱͯ͠ҎԼͷҙ - ΫϦοΫͱ࠷ॳͷϚεΦʔόʔ࣌ؒΘͳ͍ - ΫΤϦϕʔεͰΓ͍ͨͷͰ query
reformulation Θͳ͍ ֶ෦ੜʹղ͔ͤͨը૾ݕࡧλεΫʹରͯ͠ GBDT Ͱ 10-fold CV 21
༧ଌੑೳߴ͘ͳ͍͕ෆՄೳͰͳͦ͞͏ Baseline majority ʹશ෦دͤΔͱ͍͏ͷ Ref: https://arxiv.org/abs/1711.09559 22
·ͱΊͱॴײ 23
·ͱΊʢ࠶ܝʣ 1. text base ͷΣϒը૾ݕࡧͷҙਤྨՄೳ͔ʁ → YES. 3ͭʹྨ: Entertain, Explore/Learn,
Locate/Acquire 2. औಘՄೳͳಛྔ͔ΒҙਤΛผͰ͖Δ͔ʁ → YES. ཹ࣌ؒϚεδΣενϟ 3. ηογϣϯॳظͰݕࡧҙਤΛ༧ଌͰ͖Δ͔ → MAYBE. ಛྔΛͬͯϞσϧΛ࡞ͯ͠Ұఆͷੑೳ 24
ॴײ » γϯϓϧͳج४ͰྨΛ͍ͯ͠Δͱ͍͏ͷྑ͍ » ৽ͱ͍͏Θ͚Ͱͳ͍͕ҰͭҰ͔ͭͬ͠Γௐ͍ͯΔ » ࣮αʔϏεͷԠ༻ʹҰาඈ༂͕ඞཁͦ͏ʢ༧ଌੑೳͳͲʣ » ٱʑʹࣜΛશવΘͳ͍จΛಡΜͰ৽ͩͬͨ 25