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オンライン広告におけるCTR/CVR推定関係の論文を30本くらい雑に紹介する / rtb-pa...

todesking
December 23, 2019

オンライン広告におけるCTR/CVR推定関係の論文を30本くらい雑に紹介する / rtb-papers-ctr

todesking

December 23, 2019
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  1. CTR/CVRਪఆ • Click Through Rate/Conversion Rate • ޿ࠂΛݟͨͱ͖ͷϢʔβͷԠ౴Λ༧ଌ͢ΔͷͰɺ·ͱΊͯ user response

    predictionͱ͔ݺ͹ΕΔ • ޿ࠂͷΫϦοΫ/ίϯόʔδϣϯ(঎඼ͷߪೖͳͲɺͦͷ޿ ࠂ͕ୡ੒͍ͨ͠໨త)Λ༧ଌ͢ΔλεΫ • యܕྫ: WebϖʔδpʹϢʔβu͕དྷ๚ͨ͠ɻ޿ࠂaΛදࣔ ͨ͠৔߹ɺΫϦοΫ/ίϯόʔδϣϯ͕ൃੜ͢Δ֬཰͸͍ ͘Β͔ • ಛ௃ྔ͸ΧςΰϦΧϧσʔλ͕ଟ͍
  2. CTR/CVRਪఆ • ҰൠతʹɺCVRਪఆͷ΄͏͕೉͍͠ • ਖ਼ྫ͕গͳ͍ • ΫϦοΫ཰Ͱ΋~1%ఔ౓ɺCVR͸͞Βʹ௿͍ • CVͷఆٛ͸ଟ༷ •

    ಛఆϖʔδ΁ͷΞΫηε͔Β঎඼ͷߪೖ·Ͱ༷ʑ • ΞτϦϏϡʔγϣϯϞσϧ • ෳ਺ͷimp/clickͷޙCVͨ͠৔߹ɺͲͷimpͷ੒Ռʹ͢Δ͔ • ͍Ζ͍ΖͳϞσϧ͕͋Δ(࠷ޙʹΫϦοΫ͞Εͨ΋ͷΛ࠾༻ɺෳ਺ͷ impʹ੒ՌΛ෼഑) • ஗ԆϑΟʔυόοΫ • imp ʙ click͸͍͍ͤͥ਺෼͕ͩɺCV͢Δʹ͸΋ͬͱ௕͍͕͔͔࣌ؒΔ • ʮෛྫʯͱʮCVσʔλະ౸ணʯͷݟ෼͚͕͔ͭͳ͍
  3. Evaluating and Optimizing Online Advertising: Forget the click, but there

    are good proxies by Brian Dalessandro et al. SSRN 2012. • "ΦϯϥΠϯ޿ࠂͷධՁͱ࠷దԽ: ΫϦοΫΑΓྑ͍୅ସࢦඪ" • ͋Δϒϥϯυͷ޿ࠂΛݟͨਓ͕7೔Ҏ಺ʹͦͷϒϥϯυͷ঎඼Λങ ͏͔Λ༧ଌ͢Δέʔε • CVσʔλ͸εύʔεա͗ͯେม • ؆୯ʹखʹೖͬͯɺCVͱ૬͕ؔ͋ΔΑ͏ͳ୅ସࢦඪ(proxy)͕͋Δ ͱخ͍͠ • ௚ײతʹ͸ΫϦοΫ͕୅ସͱͳΓͦ͏͕ͩɺ࣮͸΄΅ؔ܎ͳ͍ • ΫϦΤΠςΟϒͷσβΠϯ͕ΫϦοΫ࠷దԽΛ໨తͱ͍ͯ͠Δ ͔ΒͰ͸? • ͦͷ୅ΘΓʹɺϒϥϯυαΠτ΁ͷ๚໰͸Α͍୅ସࢦඪͱͳΔ
  4. Estimating Conversion Rate in Display Advertising from Past Performance Data

    by Kuang-chih Lee et al. KDD 2012. • "աڈͷύϑΥʔϚϯεσʔλ͔ΒͷCVRਪఆ" • ϢʔβɺWebϖʔδɺ޿ࠂͷ૊Έ߹Θ͔ͤΒCVRΛ༧ଌ͢ Δ • ͦΕͧΕΛ֊૚తʹ෼ׂͯ͠աڈͷύϑΥʔϚϯεΛूܭ • ϩδεςΟοΫճؼͰͦΕΒΛ౷߹ͯ͠CVR༧ଌ
  5. Ad Click Prediction: a View from the Trenches by H.

    Brendan McMahan. KDD 2013. • "ΫϦοΫਪఆ: ᆠߺ͔ΒͷோΊ" • Google • େن໛ͳCTR༧ଌϞσϧͷӡ༻͔ΒಘΒΕͨ஌ݟͷ঺հ • ϩδεςΟοΫճؼͷΦϯϥΠϯֶश(FTRL-Proximal, Per- coordinate learning rates) • ϝϞϦઅ໿ͷٕ๏ • ϞσϧධՁख๏ • ༧ଌͷ৴པ౓Λද͢uncertainty score • ༧ଌͷΩϟϦϒϨʔγϣϯ • ಛ௃ྔͷ؅ཧ • ͏·͍͔͘ͳ͔ٕͬͨ๏
  6. Practical Lessons from Predicting Clicks on Ads at Facebook by

    Xinran He et al. ADKDD 2014. • "Facebookʹ͓͚ΔΫϦοΫ༧ଌ͔Βͷڭ܇" • GBDTͷ݁ՌΛઢܗճؼͰ࢖͏stackingϞσϧ • ઢܗճؼ͸LRͱBOPR • BOPR: Bayesian online learning scheme for probit regression • σʔλ઱౓͕ॏཁͳͷͰΦϯϥΠϯֶश͢Δ • Online joiner: ϩάσʔλΛΦϯϥΠϯֶशػʹ৯Θͨ͢ ΊͷγεςϜ • ϞσϧαΠζͷ࡟ݮ • ֶशσʔλͷμ΢ϯαϯϓϦϯά
  7. Modeling Delayed Feedback in Display Advertising by Olivier Chapelle. KDD

    2014. • "σΟεϓϨΠ޿ࠂʹ͓͚Δ஗ԆϑΟʔυόοΫϞσϦϯ ά" • Criteo • CV͸impޙ௕࣌ؒܦ͔ͬͯΒൃੜ͢Δ • ͜ͷ஗ԆΛߟྀͨ͠CV༧ଌϞσϧΛ࡞ͬͨ
  8. Scalable Hands-Free Transfer Learning for Online Advertising by Brian Dalessandro

    et al. KDD 2014. • "ΦϯϥΠϯ޿ࠂʹ͓͚Δεέʔϥϒϧͳख͍ؒΒͣసҠֶश" • DistilleryͷCVR༧ଌγεςϜ • Ωϟϯϖʔϯ͝ͱʹϞσϧ͕͋Δ • ίʔϧυελʔτ໰୊ɺCVͷଟ༷ੑɺϝϯςͷखؒͳͲͷ໰ ୊ʹରॲ • Bayesian transfer learning: ࣄલ෼෍ʹΑΓίʔϧυελʔτ Λ؇࿨ • ϋΠύʔύϥϝʔλෆཁͳSGD LR(NoPesky learning rates + Adaptive regularization)
  9. Scalable Hierarchical Multitask Learning Algorithms for Conversion Optimization in Display

    Advertising by Amr Ahmed et al. WSDM 2014. • "CV࠷దԽͷͨΊͷεέʔϥϒϧͳ֊૚ϚϧνλεΫֶश" • Google • ؔ࿈ͨ͠໰୊͸·ͱΊͯղ͍ͨ΄͏͕ੑೳ͕ྑ͍ • CV(attributed/non-attributed)ɺΫϦοΫΛҰ౓ʹ༧ଌ͢Δ Ϟσϧ • Multitask-learning • ֊૚తͳ֬཰ϞσϧͰΩϟϯϖʔϯɾλεΫؒͷґଘΛදݱ • ֊૚ϚϧνλεΫֶशΛ෼ࢄॲཧͰεέʔϧͤ͞Δ࿩
  10. Predicting Response in Mobile Advertising with Hierarchical Importance-Aware Factorization Machine

    by Richard J. Oentaryo et al. WSDM 2014. • "֊૚తImportance-aware FMʹΑΔϞόΠϧ޿ࠂͷϨεϙϯε ༧ଌ" • Λਪఆ͍ͨ͠(Ͳ͏͍͏ঢ়گઃఆ??) • page, adʹ͸֊૚ߏ଄͕͋Δ • ֊૚ͷ֤ϊʔυʹରԠ͢ΔॏΈΛਖ਼ଇԽͰ੍ޚ(ಉ͡਌ʹଐ͢ ΔϊʔυͷॏΈ͸͍ۙ) • ֊૚͝ͱʹू໿ֶͨ͠शσʔλΛ࡞Γɺαϯϓϧͷଟ͞Λॏཁ ౓ͱͯ͠ॏΈ෇ֶ͚ͯ͠श • Ͳ͔ͬͰtemporal dynamicsΛߟྀͯ͠ΔΒ͍͕͠ݟࣦͬͨ P(click|page, ad, day)
  11. Simple and Scalable Response Prediction for Display Advertising by Olivier

    Chapelle Criteo, Eren Manavoglu, Romer Rosales. ACM TIST 2014. • "σΟεϓϨΠ޿ࠂʹ͓͚ΔγϯϓϧͰεέʔϥϒϧͳԠ౴༧ଌ" • Criteo+MS+LinkedIn • LRͰCTR/CVR͢Δ࿩ • Bayesian LRͰThompson sampling͢Δ࿩୊΋
  12. Offline Evaluation of Response Prediction in Online Advertising Auctions by

    Olivier Chapelle. WWW 2015. • "ΦϯϥΠϯ޿ࠂΦʔΫγϣϯʹ͓͚ΔԠ౴༧ଌͷΦϑϥ ΠϯධՁ" • Criteo • CTRਪఆͷධՁͱͯ͠͸Ұൠతͳڭࢣ෇ֶ͖शͷࢦඪ͕࢖ ΘΕ͍ͯΔ͕ɺਪఆ݁Ռ͕Ͳ͏࢖ΘΕΔͷ͔ߟ͑Δ΂͖ • ਪఆͨ͠CTRʹΑͬͯbidՁ֨ΛܾΊΔγφϦΦʹ͓͚Δ ධՁࢦඪͱͯ͠expected utility[5]ΛఏҊɺ࣮σʔλͰݕূ
  13. Deep Learning over Multi-field Categorical Data: A Case Study on

    User Response Prediction by Weinan Zhang, Tianming Du, Jun Wang. ECIR 2016. • "ϚϧνϑΟʔϧυΧςΰϦΧϧσʔ λͷਂ૚ֶश: ϢʔβԠ౴ਪఆͷࣄྫ" • ը૾΍Ի੠ͱҟͳΓɺ޿ࠂͰ࢖͏ σʔλ͸ෳ਺ϑΟʔϧυͷΧςΰϦΧ ϧ஋ • ͜ΕΒͷަޓ࡞༻ͷ͏ͪ༗ޮͳ΋ͷ ΛࣗಈͰൃݟ͍ͨ͠ • Factorization Machine supported NN ͓Αͼ Sampling-based NNͱ͍͏Ϟ σϧΛఏҊ
  14. Factorization Machines with Follow-The- Regularized-Leader for CTR prediction in Display

    Advertising by Anh-Phuong Ta. BigData 2015. • "FMͱFTRLʹΑΔCTRਪఆ" • ΦϯϥΠϯ࠷దԽΞϧΰϦζϜͰ͋ΔFTRL-ProximalΛ FMʹద༻ͨ͠FTRFL(Follow-The-Regularized-Factorized- Leader)ͷఏҊ • FMͷΦϯϥΠϯֶश͕ߴ଎+ߴੑೳʹͳͬͨ
  15. A Convolutional Click Prediction Model by Qiang Liu, Feng Yu,

    Shu Wu, Liang Wang. CIKM 2015. • "৞ΈࠐΈΫϦοΫ༧ଌϞσϧ" • imp୯ମͰ͸ͳ͘ܥྻΛ࢖ͬͯCTR༧ଌ͢ΔϞσϧ • impͷܥྻʹରԠ͢Δಛ௃ྔΛೖྗɺ৞ΈࠐΈ͢Δ
  16. Cost-sensitive Learning for Utility Optimization in Online Advertising Auctions by

    Flavian Vasile, Damien Lefortier, Olivier Chapelle. Extension under-review of the paper presented at the Workshop on E-Commerce, NIPS 2015. • "ίετΛߟֶྀͨ͠शʹΑΔΦϯϥΠϯ޿ࠂͷutility࠷దԽ" • Criteo+FB+Google • CTRਪఆͷੑೳ͸utilityͰଌΔͷ͕࣮৘ʹ߹͍ͬͯΔ • ͩͬͨΒ࠷ॳ͔ΒutilityΛ࠷దԽ͍ͨ͠ • ॏΈ෇͖log lossΛ࠷దԽ͢Δ͜ͱͰۙࣅతʹutilityΛ࠷దԽ Ͱ͖Δ • ղઆ: https://k11i.biz/blog/2017/08/15/2017-adkdd-criteo/
  17. User Response Learning for Directly Optimizing Campaign Performance in Display

    Advertising by Kan Ren, Weinan Zhang, Yifei Rong, Haifeng Zhang, Yong Yu, Jun Wang. CIKM 2016. • "ΩϟϯϖʔϯͷύϑΥʔϚϯεΛ௚઀࠷దԽ͢ΔͨΊͷ ϢʔβԠ౴ͷֶश" • CTR༧ଌϞσϧ͸޿ࠂ഑৴γεςϜͷҰ෦Ͱ͋Δ • Ϟσϧ୯ମΛ࠷దԽ͢ΔΑΓɺγεςϜશମΛ࠷దԽͨ͠ ͍ • LRʹΑΔCTR༧ଌΛؚΉ഑৴γεςϜΛϞσϧԽ͠ɺ ΩϟϯϖʔϯͷརӹΛ௚઀࠷దԽͨ͠ • A/BςετͰ΋޷੒੷
  18. Predicting ad click-through rates via feature-based fully coupled interaction tensor

    factorization by Lili Shan, Lei Lin, Chengjie Sun, Xiaolong Wang. Electronic Commerce Research and Applications 2016. • "Feature-based fully coupled interaction tensor factorizationʹΑ ΔCTR༧ଌ" • (Ϣʔβ, Webϖʔδ, ޿ࠂ)Λςϯιϧͱͯ͠දݱɺCTRਪఆΛς ϯιϧิ׬໰୊ͱͯ͠ղ͘ • Fully-Coupled interactions Tensor Factorizationͱ͍͏ΞϧΰϦζ ϜΛఏҊ
  19. Bid-aware Gradient Descent for Unbiased Learning with Censored Data in

    Display Advertising by Weinan Zhang, Tianxiong Zhou, Jun Wang, Jian Xu. KDD 2016. • "ܽଛͷ͋ΔσʔλͷͨΊͷೖࡳߟྀܕޯ഑߱Լ๏ʹΑΔ όΠΞεͳֶ͠श" • RTB޿ࠂʹ͓͍ͯɺCTRਪఆʹ࢖ΘΕΔσʔλ͔Β͸ʮམ ࡳͰ͖ͳ͔ͬͨimpʯ͕ܽམ͍ͯ͠Δ(Censored data) • ੜଘ෼ੳΛద༻ͯ͠ɺ͜ΕΒͷܽଛΛߟྀͨ͠SGDΛՄ ೳʹ͢Δख๏ͷఏҊ
  20. Deep CTR Prediction in Display Advertising by Junxuan Chen et

    al. MM 2016. • "ਂ૚CTRਪఆ" • Alibaba • ඪ४తͳಛ௃ྔʹՃ͑ͯόφʔը૾Λ࢖༻ͨ͠CTRਪఆ
  21. Sparse Factorization Machines for Click- through Rate Prediction by Zhen

    Pan et al. ICDM 2016. • "εύʔεFMʹΑΔCTR༧ଌ" • CTRਪఆʹ࢖͏σʔλ͸ඇৗʹεύʔε • ͜ͷΑ͏ͳσʔλʹରԠͨ͠Sparse Factorization MachinesͷఏҊ • ύϥϝʔλͷ෼෍ͱͯ͠ਖ਼ن෼෍ͷ͔ΘΓʹϥϓϥε෼෍ Λ࢖༻(εύʔεσʔλʹڧ͍) • Spark্Ͱͷ෼ࢄֶशΛ࣮૷
  22. Product-based Neural Networks for User Response Prediction by Yanru Qu

    et al. ICDM 2016. • "ProductϕʔεNNʹΑΔϢʔβԠ౴༧ଌ" • Product layerʹ͓͍ͯɺ಺ੵ/֎ੵΛ࢖ͬͯಛ௃ྔͷม׵Λ͍ͯ͠Δ
  23. Field-aware Factorization Machines in a Real- world Online Advertising System

    by Yuchin Juan, Damien Lefortier, Olivier Chapelle. ArXiv 2017. • "ΦϯϥΠϯ޿ࠂγεςϜʹ͓͚ΔFFM" • ࣮γεςϜͷCTR/CVR༧ଌʹFFMΛಋೖͨ͠ • ෼ࢄֶश • ϞσϧΛ࠶ֶश͢Δͱ͖ʹաڈͷύϥϝʔλΛ࠶ར༻͢Δ warm-startingख๏
  24. Neural Feature Embedding for User Response Prediction in Real-Time Bidding

    (RTB) by Enno Shioji, Masayuki Arai. ArXiv 2017. • "ϢʔβԠ౴ਪఆͷͨΊͷχϡʔϥϧຒΊࠐΈ" • Ϣʔβͷಛ௃Λදݱͨ͠෼ࢄදݱΛ࡞Γ͍ͨ • ϢʔβͷӾཡཤྺΛݩʹɺwebϖʔδΛ୯ޠͱݟͳͯ͠ CBOWͰϕΫτϧԽ • Ϣʔβ͕Ӿཡͨ͠ίϯςϯπͷϕΫτϧΛ౷߹ͯ͠Ϣʔβ ͷຒΊࠐΈΛಘΔ(ਖ਼نԽ+ฏۉ) • ྨࣅ: Tagami, Yukihiro, Hayato Kobayashi, Shingo Ono and Akira Tajima. “Distributed Representations of Web Browsing Sequences for Ad Targeting.” (2016).
  25. SEM: A Softmax-based Ensemble Model for CTR Estimation in Real-Time

    Bidding Advertising by Wen-Yuan Zhu et al. BigComp 2017. • "SEM: SoftmaxϕʔεΞϯαϯϒϧϞσϧʹΑΔCTRਪఆ" • ಛ௃ྔ͕ΧςΰϦΧϧσʔλͷ৔߹ɺtest࣌ʹॳΊͯग़Δ ஋͸ແࢹ͞ΕΔͱ͍͏໰୊ • Hashing trickͰಛ௃ྔΛϕΫτϧԽˠࣗಈಛ௃ྔબ୒ˠಛ ௃ྔ͝ͱʹφΠʔϒϕΠζͯ͠Softmax • ৽ن஋໰୊͕hashing trickͰղܾ͢Δͱ͍͏ओு͸Θ͔Β ͳ͍Ͱ͢Ͷ…… • શମతʹṖ
  26. Learning Piece-wise Linear Models from Large Scale Data for Ad

    Click Prediction by Kun Gai, Xiaoqiang Zhu, Han Li, et al. Arxiv 2017. • "CTR༧ଌͷͨΊͷPeace-wiceઢܕϞσϧͷେن໛ֶश" • Alibaba • 2012೥ҎདྷAlibabaͰ࢖ΘΕ͍ͯΔCTR༧ଌϞσϧ • Linear Modelͱݴ͍ͭͭඇઢܗͳݱ৅Λѻ͑Δ • ಛ௃ۭؒΛ෼ׂ͠ɺྖҬ͝ͱʹઢܕϞσϧΛద༻͢Δ • େن໛σʔλͷֶशʹ଱͑Δ σ: dividing function η: fitting function
  27. DeepFM: A Factorization-Machine based Neural Network for CTR Prediction by

    Huifeng Guo et al. IJCAI 2017 • "DeppFM: CTR༧ଌͷͨΊͷFMϕʔεNN" • ಛ௃ΤϯδχΞϦϯάෆཁͱ͔ڧ͍͜ͱ͕ॻ͍ͯ͋Δ • Wide and Deepͷൃలܥ • ୯ମͷಛ௃ྔ΋ߴ࣍ͷަޓ࡞༻΋େࣄʹ͍ͨ͠ͱ͍͏ؾ࣋ͪ • ղઆ: https://data.gunosy.io/entry/deep-factorization-machines-2018
  28. Deep Interest Network for Click-Through Rate Prediction by Guorui Zhou

    et al. ArXiv 2017. • "CTRਪఆͷͨΊͷDeep Interest Network" • Alibaba • ϢʔβͷߦಈཤྺΛݩʹCTRਪఆ͍ͨ͠ • ࣌ܥྻσʔλΛݩʹɺϢʔβͷࠓͷঢ়ଶΛϞσϦϯά͢Δ
  29. An Ensemble-Based Approach to Click- Through Rate Prediction for Promoted

    Listings at Etsy by Kamelia Aryafar et al. ADKDD 2017. • "EtsyͷϦεςΟϯά޿ࠂʹ͓͚ΔΞϯαϯϒϧϕʔεCTRਪఆ" • ΦϯϥΠϯγϣοϐϯάαΠτEtsyʹ͓͚ΔCTR༧ଌ • ϩδεςΟοΫճؼ+FTRL-Proxima • ෳ਺ͷํ๏ͰCTRΛਪఆͯ͠ϩδεςΟοΫճؼͰ݁߹ • աڈσʔλΛεϜʔδϯάͯ͠ਪఆͨ͠CTR(σʔλ͕ଟ͍ͱྑ͘ ޮ͘) • ςΩετσʔλ͸unigram/bigramΛhashing trickͯ͠ϕΫτϧԽ • ը૾͸DNNͰຒΊࠐΈ
  30. A Practical Framework of Conversion Rate Prediction for Online Display

    Advertising by Quan Lu et al. ADKDD 2017. • CVR༧ଌʹؔ͢Δ༷ʑͳख๏ͷఏҊ • Over prediction΁ͷରԠ • delayed feedbackΛߟֶྀͨ͠श • ΞτϦϏϡʔγϣϯϞσϧΛߟྀͨ͠ೖࡳֹิਖ਼
  31. Ranking and Calibrating Click-Attributed Purchases in Performance Display Advertising by

    Sougata Chaudhuri et al. ADKDD 2017. • click-attributionͰCV՝ۚϞσϧͷ޿ࠂ഑৴ • bidͷࡍʹ͸ҎԼͷखॱ͕ඞཁ • CV͞Ε΍͍͢޿ࠂͷީิΛྻڍ(ΞτϦϏϡʔγϣϯͷ੍໿ ͰɺΫϦοΫ཰΋ߴ͍ඞཁ͕͋Δ) • CV཰ʹԠͯ͡ೖࡳֹۚΛܾఆ(ਖ਼֬ͳCV཰ͷਪఆ͸ࠔ೉) • ϥϯΩϯά+ΩϟϦϒϨʔγϣϯʹΑͬͯ͜ͷ໰୊Λղ͘
  32. Deep & Cross Network for Ad Click Predictions by Ruoxi

    Wang et al. ADKDD & TargetAd 2017. • Google • Deep & Cross NetworkϞσ ϧͷఏҊ • ߴ࣍ͷަޓ࡞༻Λཅʹѻ͏ ωοτϫʔΫ(Cross)Λ࣋ͭ
  33. Field-weighted Factorization Machines for Click-Through Rate Prediction in Display Advertising

    by Junwei Pan et al. WWW 2018. • Field-aware FM͸ੑೳ͍͍͕ύϥϝʔλ਺ଟա͗ͳͷͰվ ળͨ͠ • ղઆ: https://www.smartbowwow.com/2019/06/field- weighted-factorization-machines.html
  34. Robust Factorization Machines for User Response Prediction by Surabhi Punjabi

    and Priyanka Bhatt. WWW 2018. • Webͷಛੑ্ɺϢʔβΛਖ਼֬ʹࣝผͰ͖ͳ͍ • ผΞϓϦ/σόΠε͔ΒͷΞΫηε • CookieແޮԽ • ϩόετ࠷దԽͱݺ͹ΕΔख๏ΛFMʹద༻ɺֶशσʔλ ͷϊΠζʹڧ͍ϞσϧΛಘΒΕͨ • ஶऀʹΑΔղઆ: https://medium.com/@priyankabhatt91/ robust-factorization-machines-b44e2d906f15
  35. A Nonparametric Delayed Feedback Model for Conversion Rate Prediction by

    Yuya Yoshikawa and Yusaku Imai. ArXiv 2018. • CVRਪఆʹ͓͍ͯ͸ɺϑΟʔυόοΫͷ஗Ԇ͕໰୊ʹͳΔ • imp͔ͯ͠Β௕࣌ؒ଴ͨͳ͍ͱCV͢Δ͔Ͳ͏͔Θ͔Βͳ͍ • ਖ਼ֶ͍͠शͷͨΊʹ͸࣌ؒ஗Εͷ෼෍Λਪఆ͢Δඞཁ͕͋Δ͕ɺ ಛఆͷ෼෍ΛԾఆͤͣϊϯύϥϝτϦοΫʹ΍Γ͍ͨ • CVRਪఆϞσϧʹϊϯύϥ஗ԆϞσϧΛ૊ΈࠐΜͩख๏ΛఏҊ • ೔ຊޠ: https://www.jstage.jst.go.jp/article/pjsai/JSAI2018/0/ JSAI2018_1N202/_article/-char/ja/