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
700
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
480
20201121_oldpaperreading_computing_machinery_and_intelligence
diracdiego
0
160
20200906_ACL2020_metric_for_ordinal_classification_YoheiKikuta
diracdiego
1
1.3k
20191102_ACL2019_adversarial_examples_in_NLP_YoheiKIKUTA
diracdiego
2
1.4k
20190223_nlpaperchallenge_CV_4.3to5.5
diracdiego
2
800
20180701_CVPR2018_reading_YoheiKIKUTA
diracdiego
3
1.2k
20180306_NIPS2017_DeepLearning
diracdiego
4
5.9k
20180215_MLKitchen7_YoheiKIKUTA
diracdiego
0
420
20180210_Cookpad_TechConf2018_YoheiKIKUTA
diracdiego
5
1.2k
Other Decks in Science
See All in Science
Transformers are Universal in Context Learners
gpeyre
0
800
学術講演会中央大学学員会いわき支部
tagtag
0
150
Trend Classification of InSAR Displacement Time Series Using SAE–CNN
satai
3
290
構造設計のための3D生成AI-最新の取り組みと今後の展開-
kojinishiguchi
1
1.1k
ガウス過程回帰とベイズ最適化
nearme_tech
PRO
1
320
白金鉱業Meetup Vol.16_数理最適化案件のはじめかた・すすめかた
brainpadpr
3
1.6k
メール送信サーバの集約における透過型SMTP プロキシの定量評価 / Quantitative Evaluation of Transparent SMTP Proxy in Email Sending Server Aggregation
linyows
0
870
[第62回 CV勉強会@関東] Long-CLIP: Unlocking the Long-Text Capability of CLIP / kantoCV 62th ECCV 2024
lychee1223
1
910
モンテカルロDCF法による事業価値の算出(モンテカルロ法とベイズモデリング) / Business Valuation Using Monte Carlo DCF Method (Monte Carlo Simulation and Bayesian Modeling)
ikuma_w
0
120
01_篠原弘道_SIPガバニングボード座長_ポスコロSIPへの期待.pdf
sip3ristex
0
350
Iniciativas independentes de divulgação científica: o caso do Movimento #CiteMulheresNegras
taisso
0
1.4k
Causal discovery based on non-Gaussianity and nonlinearity
sshimizu2006
0
260
Featured
See All Featured
Making Projects Easy
brettharned
116
6.2k
YesSQL, Process and Tooling at Scale
rocio
172
14k
Imperfection Machines: The Place of Print at Facebook
scottboms
267
13k
Thoughts on Productivity
jonyablonski
69
4.6k
Visualization
eitanlees
146
16k
The Power of CSS Pseudo Elements
geoffreycrofte
75
5.8k
Optimizing for Happiness
mojombo
378
70k
10 Git Anti Patterns You Should be Aware of
lemiorhan
PRO
656
60k
Gamification - CAS2011
davidbonilla
81
5.3k
Done Done
chrislema
184
16k
A Tale of Four Properties
chriscoyier
159
23k
Code Reviewing Like a Champion
maltzj
523
40k
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