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
20180414_WSDM2018_reading_YoheiKIKUTA
Search
yoppe
April 12, 2018
Science
0
690
20180414_WSDM2018_reading_YoheiKIKUTA
HP:
https://atnd.org/events/95510
yoppe
April 12, 2018
Tweet
Share
More Decks by yoppe
See All by yoppe
20211023_recsys2021_paper_reading_YoheiKikuta
diracdiego
2
450
20201121_oldpaperreading_computing_machinery_and_intelligence
diracdiego
0
150
20200906_ACL2020_metric_for_ordinal_classification_YoheiKikuta
diracdiego
1
1.2k
20191102_ACL2019_adversarial_examples_in_NLP_YoheiKIKUTA
diracdiego
2
1.4k
20190223_nlpaperchallenge_CV_4.3to5.5
diracdiego
2
760
20180701_CVPR2018_reading_YoheiKIKUTA
diracdiego
3
1.1k
20180306_NIPS2017_DeepLearning
diracdiego
4
5.8k
20180215_MLKitchen7_YoheiKIKUTA
diracdiego
0
380
20180210_Cookpad_TechConf2018_YoheiKIKUTA
diracdiego
5
1.1k
Other Decks in Science
See All in Science
私たちのプロダクトにとってのよいテスト/good test for our products
camel_404
0
180
Pericarditis Comic
camkdraws
0
1.2k
論文紹介: PEFA: Parameter-Free Adapters for Large-scale Embedding-based Retrieval Models (WSDM 2024)
ynakano
0
150
Snowflakeによる統合バイオインフォマティクス
ktatsuya
0
490
20240420 Global Azure 2024 | Azure Migrate でデータセンターのサーバーを評価&移行してみる
olivia_0707
2
900
Transformers are Universal in Context Learners
gpeyre
0
550
JSol'Ex : traitement d'images solaires en Java
melix
0
110
Celebrate UTIG: Staff and Student Awards 2024
utig
0
470
【人工衛星】座標変換についての説明
02hattori11sat03
0
110
Causal discovery based on non-Gaussianity and nonlinearity
sshimizu2006
0
190
事業会社における 機械学習・推薦システム技術の活用事例と必要な能力 / ml-recsys-in-layerx-wantedly-2024
yuya4
3
230
インフラだけではない MLOps の話 @事例でわかるMLOps 機械学習の成果をスケールさせる処方箋 発売記念
icoxfog417
2
580
Featured
See All Featured
Designing on Purpose - Digital PM Summit 2013
jponch
115
7k
Teambox: Starting and Learning
jrom
133
8.8k
The Power of CSS Pseudo Elements
geoffreycrofte
73
5.3k
Building Better People: How to give real-time feedback that sticks.
wjessup
364
19k
Cheating the UX When There Is Nothing More to Optimize - PixelPioneers
stephaniewalter
280
13k
A Tale of Four Properties
chriscoyier
156
23k
JavaScript: Past, Present, and Future - NDC Porto 2020
reverentgeek
47
5k
The Art of Programming - Codeland 2020
erikaheidi
52
13k
No one is an island. Learnings from fostering a developers community.
thoeni
19
3k
4 Signs Your Business is Dying
shpigford
180
21k
Making the Leap to Tech Lead
cromwellryan
133
8.9k
Reflections from 52 weeks, 52 projects
jeffersonlam
346
20k
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