Upgrade to Pro — share decks privately, control downloads, hide ads and more …

オンライン広告関連の論文を50本くらい雑に紹介する AdKDD編 / adkdd-all

Eb54d1a9d9a9d1b25a62f1387c49a315?s=47 todesking
December 13, 2019
1.8k

オンライン広告関連の論文を50本くらい雑に紹介する AdKDD編 / adkdd-all

Eb54d1a9d9a9d1b25a62f1387c49a315?s=128

todesking

December 13, 2019
Tweet

Transcript

  1. ΦϯϥΠϯ޿ࠂؔ࿈ͷ࿦จΛ 50ຊ͘Β͍ࡶʹ঺հ͢Δ AdKDDฤ @todesking

  2. ࣗݾ঺հ • @todesking • DSP/ΞυωοτϫʔΫͷձࣾ ͰػցֶशΛ΍͍ͬͯΔ • ೔ࠒͷٕज़ௐ͕ࠪ଍Γͳ͍ͳ ͱࢥͬͨͷͰAdKDDͷ࿦จΛ ோΊͯΈ·ͨ͠

  3. AdKDD/TargetADͱ͸ • KDD: Knowledge Discovery and Data Mining • σʔλϚΠχϯά΍ػցֶशʹؔ͢ΔԠ༻తͳ࿩୊Λѻ͏ࠃࡍ

    ձٞ • AdKDD: ΦϯϥΠϯ޿ࠂؔ࿈ٕज़Λѻ͏෼Պձ • TargetAd: WSDMͱ͍͏ࠃࡍձٞʹ͓͍ͯΦϯϥΠϯ޿ࠂؔ࿈ٕज़ Λѻ͏෼Պձ • ࠷ۙ͸AdKDDʹٵऩ͞Εͨ? • ࠾୒࿦จ਺͸ຖ೥10ຊऑ • KDDຊମʹ΋޿ࠂܥ࿦จ͕͋Δ(੗Έ෼͚͸Ṗ)
  4. ஫ҙ • ཁ໿͸΄΅AbstractோΊ͚ͨͩɺਖ਼֬͞͸อূ͠·ͤΜ • ؒҧ͍ͷࢦఠ͸׻ܴ • ۙ೥ͷ࿦จ͸ެࣜαΠτ͔ΒPDFͷ௚ϦϯΫ͕͋Δ • Keynote/Invited talk͸লུ

  5. AdKDD 2014 https://dblp.org/db/conf/kdd/adkdd2014

  6. A Dynamic Pricing Model for Unifying Programmatic Guarantee and Real-Time

    Bidding in Display Advertising • publisher͕impΛചΔʹ͸RTBͱguranteed contractͱ͍ ͏ೋͭͷํ๏͕͋Δ • publisherͷऩӹΛදݱ͢Δ਺ཧϞσϧΛ࡞Γɺೋͭͷํ ๏΁ͷ༧ࢉ഑෼Λ࠷దԽ͢Δ
  7. Multi-Touch Attribution Based Budget Allocation in Online Advertising • Ωϟϯϖʔϯશମͷ༧ࢉ͕ܾ·͓ͬͯΓɺԼҐͷΩϟϯ

    ϖʔϯʹ഑෼͍ͨ͠ • ໨ඪ΁ͷߩݙʹԠͯ͡഑෼͍ͨ͠ˠlast-touch/multi- touch attributionͷ৘ใΛݩʹׂͯ͠Γ౰ͯΔํ๏Λ࣮૷ ͨ͠
  8. Pleasing the advertising oracle: Probabilistic prediction from sampled, aggregated ground

    truth • ޿ࠂΩϟϯϖʔϯͷηάϝϯτਫ਼౓Λୈࡾऀ͕ධՁ͢Δ έʔε • ධՁͱͯ͠ɺ޿ࠂΛݟͨϢʔβͷσϞάϥଐੑͷׂ߹͕ ϑΟʔυόοΫ͞ΕΔ • ݸผϢʔβͷଐੑʹ͍ͭͯͷ৘ใ͸ಘΒΕͳ͍ • ͜ͷ৘ใΛ࢖ͬͯηάϝϯτͷਫ਼౓Λվળ͢Δ
  9. The PlaceIQ Analytic Platform: Location Oriented Approaches to Mobile Audiences

    • PlaceIQͱ͍͏Ґஔϕʔεͷ޿ࠂ෼ੳπʔϧͷ঺հ
  10. Practical Lessons from Predicting Clicks on Ads at Facebook •

    ஶ໊ͳΦϯϥΠϯ޿ࠂ࿦จ • FBͷCTR༧ଌϞσϧͷ࣮૷ʹ͍ͭͯ
  11. iPinYou Global RTB Bidding Algorithm Competition Dataset • iPinYouͱ͍͏DSP͕ओ࠵ͨ͠RTBΞϧΰϦζϜͷίϯς ετͰ࢖༻͞Εͨσʔληοτʹ͍ͭͯ

    • ͜ͷσʔληοτ͸RTBܥϩδοΫͷ࣮ݧʹΑ͘࢖ΘΕͯ ͍Δ
  12. Can Television Advertising Impact Be Measured on the Web? Web

    Spike Response as a Possible Conversion Tracking System for Television. • ςϨϏࢹௌऀ͸ಉ࣌ʹΠϯλʔωοτ΋΍ΔͷͰɺςϨϏ CMͷࢹௌ͕े਺ඵޙͷωοτ׆ಈʹӨڹΛ༩͑Δ • ͜ͷΑ͏ͳӨڹΛܭଌ͢ΔͨΊͷwebϕʔεγεςϜΛ։ ൃͨ͠
  13. TargetAd 2016 https://sites.google.com/site/targetad2016/program

  14. TargetAd 2016 https://sites.google.com/site/targetad2016/program

  15. TargetAd 2016 https://sites.google.com/site/targetad2016/program

  16. Distributed Representations of Web Browsing Sequences for Ad Targeting •

    Ϣʔβͷϒϥ΢δϯάཤྺΛಛ௃ྔԽ͍ͨ͠ • ϢʔβΛparagraphɺURLΛwordʹݟཱͯͯParagraph Vector(W2Vͷ೿ੜɺύϥάϥϑͷembeddingΛಘΒΕΔ) Λߏங͢Δͱ͍͏ͷΛલճ΍ͬͨ • Backward PV-DMͱ͍͏վྑ൛ͷఏҊ
  17. Sponsored Video Advertising: Filling the Void between Online and TV

    Advertising • Ϣʔβ͕ಈըΛࢹௌ͍ͯ͠Δͱ͖ʹ޿ࠂΛݟ͍ͤͨɺݟͤ ·͘Γ͍ͨͱ͍͏ڧ͍ؾ͕࣋ͪ͋Δ • ಈըʹؔ࿈͢Δ޿ࠂͷ՝ۚମܥʹ͍ͭͯͷఏҊ
  18. On Spatial-Aware Viral Marketing for Location-based Advertisements, • ৽͍͠ళΛ։͖͍ͨ৔߹ɺళͷ৔ॴɺޱίϛͷޮՌɺ෺ ཧ޿ࠂͷ৔ॴͳͲͷཁૉ͕ച্͛ʹӨڹΛ༩͑Δɻ

    • ͦͷΑ͏ͳ੍໿ͷԼͰ࠷దͳީิΛબͿํ๏ΛఏҊ
  19. Keynote: Come back soon! Estimating return times for users •

    ߦಈཤྺΛݩʹɺϢʔβ͕࣍ʹ͜ͷαʔϏεΛ࢖͏ͷ͸ ͍͔ͭਪఆ͍ͨ͠ • ੜଘ෼ੳɺ఺աఔɺLSTM
  20. Building a Bi-directed Recommendation System for Mobile Users and App-install

    Ad Campaigns • ϞόΠϧΞϓϦʹؔ͢Δ૒ํ޲Ϩίϝϯσʔγϣϯγες Ϝ • ΞϓϦͷ޿ࠂΩϟϯϖʔϯʹରͯ͠ɺޮՌతͳϢʔβηά ϝϯτΛਪન • Ϣʔβʹରͯ͠ɺڵຯΛ࣋ͪͦ͏ͳΞϓϦΛਪન
  21. Finding Needle in a Million Metrics: Anomaly Detection in a

    Large-scale Computational Advertising Platform • ޿ࠂϓϥοτϑΥʔϜʹ͓͚Δҟৗݕ஌ • Ξυϑϥ΢υͷจ຺Ͱ͸ͳ͘ɺγεςϜͷҟৗΛݕग़͢Δ ͷ͕໨త • ҆ఆͨ͠ύϑΥʔϚϯεΛग़͍ͯ͠ΔΩϟϯϖʔϯΛ؍ଌ • ύϑΥʔϚϯεʹҟৗ͕͋ͬͨΒɺγεςϜଆʹݪҼ͕ ͋ΔՄೳੑ͕ߴ͍
  22. Preserving Privacy in Geo- Targeted Advertising • ஍ҬͱऩೖΛ࢖ͬͨλʔήςΟϯά޿ࠂΛߟ͑Δ • ߈ܸऀ͸τϐοΫϞσϦϯά΍ػցֶशΛ࢖͏͜ͱͰɺ͋

    Δ޿ࠂ͕දࣔ͞ΕͨϢʔβͷ஍Ҭ΍ऩೖΛਪఆͰ͖Δ • ഑৴͞ΕΔ޿ࠂʹϊΠζΛࠞͥΔ͜ͱͰ͜ͷΑ͏ͳਪఆΛ ๦͛Δ
  23. Invited talk: User Recommendation in Instagram • Instagramʹ͓͚Δ͓͢͢ΊϢʔβͷ࢓૊Έ • ιʔγϟϧάϥϑͷߏ଄΍ڵຯͷ͋ΔτϐοΫΛ࢖ͬͨਪ

  24. Demographic Prediction of Web Requests from Labeled Aggregate Data •

    Ϣʔβू߹ʹରͯ͠ɺσϞάϥଐੑͷׂ߹Λ஌Δ͜ͱ͕Ͱ ͖Δͱ͢Δ • ͜ͷ৘ใΛ࢖ͬͯϢʔβ୯ҐͰͷଐੑਪఆΛ͢Δ • Williams+, "Pleasing the advertising oracle: Probabilistic prediction from sampled, aggregated ground truth", AdKDD2014 ͷൃల
  25. Toward Personalized Product Search for eCommerce Sites: A Case Study

    in Yahoo! Taiwan • Y!୆࿷ͷeίϚʔεαΠτʹ͓͚Δݕࡧ • ଟ͘ͷݕࡧΫΤϦ͸ҰൠతͩͬͨΓᐆດͰɺϢʔβ͸๲େ ͳ݁Ռͷத͔Β໨తͷ΋ͷΛ୳͞Ͷ͹ͳΒͳ͍ • ςΩετ৘ใ͚ͩͰͷϚονϯάͰ͸ෆे෼ͳͷͰɺϢʔ βͷڵຯʹԠͯ͡ύʔιφϥΠζͨ͠ϥϯΩϯάΛߦ͏
  26. AdKDD & TargetAd 2017

  27. AdKDD & TargetAd 2017 https://adkdd-targetad.wixsite.com/adkddtargetad2017/accepted-papers

  28. AdKDD & TargetAd 2017 https://adkdd-targetad.wixsite.com/adkddtargetad2017/accepted-papers

  29. AdKDD & TargetAd 2017 https://adkdd-targetad.wixsite.com/adkddtargetad2017/accepted-papers

  30. Attribution Modeling Increases Efficiency of Bidding in Display Advertising •

    Criteo • ΞτϦϏϡʔγϣϯϞσϧΛ࢖ͬͨbidઓུ • 2nd price auctionʹ͓͚Δ࠷దઓུ͸ͦͷϦΫΤετͷՁ ஋Λೖࡳֹͱ͢Δ͜ͱ͕ͩɺਖ਼֬ͳՁ஋ΛٻΊΔͨΊʹ ͸ΞτϦϏϡʔγϣϯϞσϧΛߟྀ͢Δඞཁ͕͋Δ
  31. Blacklisting the Blacklist in Online Advertising • Dstillery • RTBʹ͓͍ͯ͸publisherଆ͕ϒϥοΫϦετΛ͍࣋ͬͯͯ

    ಛఆͷ޿ࠂΛ͸͘͜͡ͱ͕͋Δ • ϒϥοΫϦετͷ಺༰͸ඇެ։ • ແବͳbidΛආ͚ΔͨΊϒϥοΫϦετͷ಺༰Λਪఆ͢Δ ػೳΛ࡞ͬͨ • γεςϜෛՙ͕ݮΓɺউ཰͕޲্ͨ͠
  32. Anti-Ad Blocking Strategy: Measuring its True Impact • Adobe Research

    • AdBlockΛݕग़ͯ͠ϢʔβʹϝοηʔδΛग़͢ઓུ͕͋Δ (՝ۚίʔε΁ͷ༠ಋͳͲ) • ͜ͷઓུ͸Ͳͷఔ౓༗ޮͳͷ͔ௐ΂ΔͨΊͷ౷ܭతख๏ ΛఏҊ • ͳΜΒ͔ͷࣄ৘ʹΑΓɺී௨ʹA/Bςετ͢ΔΘ͚ʹ͸͍ ͔ͳ͍Β͍͠
  33. MM2RTB: Bringing Multimedia Metrics to Real-Time Bidding • ޿ࠂͷ࣭ΛධՁ͢ΔͨΊͷmultimedia metricsͱ͍͏ͷ͕

    ͋Δ • contextual relevance, visual saliency, ad memorability • ϖʔδ಺ʹෳ਺ͷ޿ࠂεϩοτ͕͋Δͱ͖ɺmultimedia metricsΛߟྀͯ͠޿ࠂͷ૊Έ߹ΘͤΛબ୒͢Δख๏ͷఏ Ҋ
  34. Data-Driven Reserve Prices for Social Advertising Auctions at LinkedIn •

    ηΧϯυϓϥΠεΦʔΫγϣϯʹ͓͚Δ࠷దͳϦβʔϒϓ ϥΠεΛٻΊΔํ๏Λ2छྨ(Ϣʔβ/ηάϝϯτϨϕϧ)ఏ Ҋ • ࣮γεςϜʹ࣮૷ͯ͠ޮՌΛݕূͨ͠
  35. Optimal Reserve Price for Online Ads Trading Based on Inventory

    Identification • Alibaba+Yahoo • ͜Ε΋reserve priceͷܾఆઓུʹؔ͢Δ࿩
  36. Ranking and Calibrating Click-Attributed Purchases in Performance Display Advertising •

    click-attributionͰCV՝ۚϞσϧͷ޿ࠂ഑৴ • bidͷࡍʹ͸ҎԼͷखॱ͕ඞཁ • CV͞Ε΍͍͢޿ࠂͷީิΛྻڍ(ΞτϦϏϡʔγϣϯͷ ੍໿ͰɺΫϦοΫ཰΋ߴ͍ඞཁ͕͋Δ) • CV཰ʹԠͯ͡ೖࡳֹۚΛܾఆ(ਖ਼֬ͳCV཰ͷਪఆ͸ࠔ ೉) • ϥϯΩϯά+ΩϟϦϒϨʔγϣϯʹΑͬͯ͜ͷ໰୊Λղ͘
  37. Cost-sensitive Learning for Utility Optimization in Online Advertising Auctions •

    CTR/CVR༧ଌʹ͸ɺhighly non-uniform misprediction cost͕ଘࡏ͢Δ • UtilityͷΑ͏ͳࢦඪΛ࢖ͬͯੑೳධՁ͢Δख๏͕ఏҊ͞Ε ͍ͯΔ • ֶश࣌ʹ΋UtilityΛߟྀͯ͠࠷దԽ͍ͨ͠ • log lossʹॏΈΛ͚ͭΔ͜ͱͰੑೳվળ͢Δख๏ͷఏҊ
  38. A Practical Framework of Conversion Rate Prediction for Online Display

    Advertising • CVR༧ଌʹؔ͢Δ༷ʑͳख๏ͷఏҊ • Over prediction΁ͷରԠ • delayed feedbackΛߟֶྀͨ͠श • ΞτϦϏϡʔγϣϯϞσϧΛߟྀͨ͠ೖࡳֹิਖ਼
  39. An Ensemble-based Approach to Click- Through Rate Prediction for Promoted

    Listings at Etsy • Etsyͷlisting adͰ࢖ΘΕ͍ͯΔCTRਪఆγεςϜͷ঺հ
  40. Profit Maximization for Online Advertising Demand-Side Platform • CPC/CPAϞσϧʹ͓͍ͯɺDSPͷརӹΛ࠷େԽ͍ͨ͠ •

    ࠷దͳೖࡳઓུ(Ͳͷ޿ࠂΛ͍͘ΒͰೖࡳ͢Δ͔)ʹ͍ͭͯ ߟ࡯ • Ωϟϯϖʔϯ༧ࢉͱϖʔγϯάɺλʔήςΟϯάɺimpڙ څྔͷ੍໿͕͋Δ • ࣮༻తʹղ͚ΔϞσϧΛఏҊ
  41. Deep & Cross Network for Ad Click Predictions • Google

    • Deep & Cross networkͱ͍͏DLͷϞσϧΛఏҊ • ަޓ࡞༻Λཅʹѻ͑Δ • CTRਪఆʹར༻ͯ͠ߴੑೳͩͬͨ
  42. AdKDD & TargetAd 2018

  43. AdKDD & TargetAd 2018 https://adkdd-targetad.wixsite.com/2018/accepted-papers

  44. AdKDD & TargetAd 2018 https://adkdd-targetad.wixsite.com/2018/accepted-papers

  45. A Large Scale Benchmark for Uplift Modeling • Criteo •

    Uplift modelingͷݕূʹ࢖͑Δେن໛σʔλΛެ։ͨ͠ • https://ailab.criteo.com/criteo-uplift-prediction-dataset/
  46. Deep Density Networks and Uncertainty in Recommender Systems • Taboola(޿ࠂϓϥοτϑΥʔϜΛ΍ͬͯΔձࣾ)

    • Deep Density Networksͱ͍͏ϞσϧͷఏҊ • content based+ڠௐϑΟϧλϦϯάͷϋΠϒϦου • ༧ଌͷෆ࣮֬ੑΛදݱͰ͖Δ • ε-greedyͱUCBΛ૊Έ߹ΘͤͨΑ͏ͳϩδοΫͰ exploration/exploitation͢Δ
  47. Deep Neural Net with Attention for Multi-channel Multi-touch Attribution •

    DNNΛ࢖ͬͨMulti-touch attributionϞσϧͷఏҊ
  48. Deep Policy Optimization for E-commerce Sponsored Search Ranking Strategy •

    2018͔ΒDNNΛ࢖ͬͨൃද͕໨ཱͪ·͢Ͷ • εϙϯαʔυαʔνͷϥϯΩϯά໰୊Λਂ૚ڧԽֶशͰ ղ͘
  49. Designing Experiments to Measure Incrementality on Facebook • Facebookʹ͓͚Δincrementalityܭଌख๏ͷղઆ •

    upliftͱincrementalityͷҧ͍͕Ṗ • ୯ͳΔA/BςετΑΓॊೈͳ࣮ݧ͕Մೳ
  50. Dynamic Hierarchical Empirical Bayes: A Predictive Model Applied to Online

    Advertising • Adobe • εϙϯαʔυαʔνʹ͓͚ΔCTR/CVRਪఆϞσϧ • ֊૚ϕΠζʹ͓͍ͯɺσʔλʹԠͯ͡ಈతʹ֊૚ߏ଄Λܾ ఆ͢ΔΑ͏ͳϞσϧͷఏҊ
  51. Forecasting Granular Audience Size for Online Advertising • ಛఆͷηάϝϯτʹ޿ࠂ഑৴͢Δͱ͖ɺࣄલʹΦʔσΟΤ ϯεͷαΠζ͕஌Γ͍ͨ

    • Frequent Itemset Miningͱ͍͏ख๏ΛݩʹɺͦͷΑ͏ͳਪ ఆΛ͢Δख๏ΛఏҊ
  52. Mini-Batch AUC Optimization • ֶश࣌ʹAUCΛ௚઀࠷దԽ͍͕ͨ͠ɺେن໛σʔληο τʹ͓͍ͯ͸೉͔ͬͨ͠ • ϛχόονΛ࢖͏͜ͱͰߴ଎ͳֶशΛՄೳͱ͢Δख๏Λ ఏҊ

  53. Optimal Bidding, Allocation and Budget Spending for a Demand Side

    Platform Under Many Auction Types • ৽͍͠ೖࡳઓུϩδοΫͷఏҊ • 1st/2nd price auctionʹରԠɺ༧ࢉ੍໿΍ΫϥΠΞϯτ͝ ͱͷر๬ΛߟྀͰ͖Δ
  54. AdKDD 2019

  55. AdKDD 2019 https://www.adkdd.org/2019-papers-and-talks

  56. AdKDD 2019 https://www.adkdd.org/2019-papers-and-talks

  57. AdKDD 2019 https://www.adkdd.org/2019-papers-and-talks

  58. Time-Aware Prospective Modeling of Users for Online Display Advertising •

    Yahoo • ࣌ܥྻΛߟྀͨ͠ϢʔβߦಈϞσϦϯάʹΑΓɺϢʔβͷ ҙਤΛߟྀͨ͠ਪఆΛߦ͏DNNϞσϧͷఏҊ
  59. Graphing Crumbling Cookies • ΫϩεσόΠετϥοΩϯάΛ͍͕ͨ͠ɺۙ೥cookieͷ࢖ ༻੍ݶ͕ݫ͘͠ͳ͍ͬͯΔ(ITP౳) • Browser fingerprintingͳͲͷΘΔ͍ٕज़Λ࢖Θͣ͜ͷঢ়گ ʹରԠ͢ΔͨΊͷख๏ΛఏҊ

  60. Causally Driven Incremental Multi Touch Attribution Using a Recurrent Neural

    Network • JD.com(ژ౦঎৓): தࠃͷڊେECاۀ • multi-touch attributionΛRNNͰϞσϦϯά͢Δख๏Λఏ Ҋ • ͜ͷϞσϧ͸࣮ࡍ࢖ΘΕ͍ͯΔΒ͍͠
  61. Combinatorial Keyword Recommendations for Sponsored Search with Deep Reinforcement Learning

    • Baidu • ਂ૚ڧԽֶशͰϨίϝϯμ࡞Δ΍ͭ
  62. Feasible Bidding Strategies through Pure Exploration Bandits • ೖࡳઓུͷީิ͕େྔʹ͋Γɺ͍͍΍ͭΛબͼ͍ͨ •

    ύϥϝλͷ૊Έ߹ΘͤͰ͔ͳΓީิ͕ଟ͍(~100)ͨΊɺී ௨ʹόϯσΟοτΞϧΰϦζϜΛ࢖͏ͷ͸ඇޮ཰ • ʮͦͦ͜͜Αͦ͞͏ʯͳީิΛߴ଎ʹൃݟ͢ΔͨΊͷख๏ ΛఏҊ
  63. In-app Purchase Prediction Using Bayesian Personalized DwellDay Ranking • ژେ+CyberAgent

    • ΞϓϦ಺՝ۚͦ͠͏ͳϢʔβΛਪఆ͢ΔλεΫ • Bayesian personalized rankingΛݩʹͨ͠ख๏ΛఏҊ
  64. Learning from Multi-User Activity Trails for B2B Ad Targeting •

    Yahoo • B2Bʹ͓͚Δ঎඼ͷߪೖϓϩηε͸ɺෳ਺ͷਓ͕ؒؔΘͬ ͨෳࡶͳ෺ʹͳΔ • ͜ͷΑ͏ͳϓϩηεʹରԠͨ͠޿ࠂͷ഑৴ઓུ͕ඞཁ • ಉ͡૊৫ʹ͍ΔΦʔσΟΤϯεΛਪఆɺͦΕΒͷߦಈΛ૯ ߹ͯ͠CV༧ଌ͢ΔϞσϧΛఏҊ
  65. Modeling Advertiser Bidding Behaviors in Google Sponsored Search with a

    Mirror Attention Mechanism • SSPଆ͔ΒೖࡳऀͷߦಈΛϞσϦϯά͢Δ • ৽ػೳΛϦϦʔεͨ͠ࡍɺͦΕ͕௕ظతʹ޿ࠂओʹͲͷΑ ͏ͳӨڹΛٴ΅͔͢஌Γ͍ͨ…… ͱ͍͏ͷ͕Ϟνϕʔ γϣϯΒ͍͠
  66. Optimal bidding: a dual approach • ৽͍͠ೖࡳઓུͷఏҊ

  67. ·ͱΊ • ޿ࠂܥ࿦จ͕ͨ͘͞Μ͋Δ͜ͱ͕Θ͔Γ·ͨ͠ • ͍͔͕Ͱ͔ͨ͠? • དྷ೥಄͘Β͍ʹ޿ࠂܥ࿦จಡॻձΛ΍Γ͍ͨͷͰɺڵຯ͋ Δਓ͸͓੠͕͚͍ͩ͘͞