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
730
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
500
20201121_oldpaperreading_computing_machinery_and_intelligence
diracdiego
0
170
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
850
20180701_CVPR2018_reading_YoheiKIKUTA
diracdiego
3
1.3k
20180306_NIPS2017_DeepLearning
diracdiego
4
5.9k
20180215_MLKitchen7_YoheiKIKUTA
diracdiego
0
460
20180210_Cookpad_TechConf2018_YoheiKIKUTA
diracdiego
5
1.2k
Other Decks in Science
See All in Science
Text-to-SQLの既存の評価指標を問い直す
gotalab555
1
160
次代のデータサイエンティストへ~スキルチェックリスト、タスクリスト更新~
datascientistsociety
PRO
2
25k
データマイニング - ノードの中心性
trycycle
PRO
0
320
データベース04: SQL (1/3) 単純質問 & 集約演算
trycycle
PRO
0
1.1k
Cross-Media Technologies, Information Science and Human-Information Interaction
signer
PRO
3
31k
NDCG is NOT All I Need
statditto
2
2.7k
People who frequently use ChatGPT for writing tasks are accurate and robust detectors of AI-generated text
rudorudo11
0
180
Performance Evaluation and Ranking of Drivers in Multiple Motorsports Using Massey’s Method
konakalab
0
130
My Little Monster
juzishuu
0
470
データマイニング - グラフ構造の諸指標
trycycle
PRO
0
240
機械学習 - 決定木からはじめる機械学習
trycycle
PRO
0
1.2k
Algorithmic Aspects of Quiver Representations
tasusu
0
160
Featured
See All Featured
Ethics towards AI in product and experience design
skipperchong
1
170
The Success of Rails: Ensuring Growth for the Next 100 Years
eileencodes
47
7.9k
Mozcon NYC 2025: Stop Losing SEO Traffic
samtorres
0
120
The Hidden Cost of Media on the Web [PixelPalooza 2025]
tammyeverts
2
140
Put a Button on it: Removing Barriers to Going Fast.
kastner
60
4.1k
Building Adaptive Systems
keathley
44
2.9k
Marketing Yourself as an Engineer | Alaka | Gurzu
gurzu
0
110
GraphQLの誤解/rethinking-graphql
sonatard
74
11k
Un-Boring Meetings
codingconduct
0
180
Collaborative Software Design: How to facilitate domain modelling decisions
baasie
0
120
HU Berlin: Industrial-Strength Natural Language Processing with spaCy and Prodigy
inesmontani
PRO
0
140
The Director’s Chair: Orchestrating AI for Truly Effective Learning
tmiket
1
79
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