ΦϯϥΠϯ޿ࠂʹ͓͚Δ CTR/CVRਪఆؔ܎ͷ࿦จΛ 30ຊ͘Β͍ࡶʹ঺հ͢Δ rtb-papersฤ @todesking 

rtb-papersͱ͸ • ্ւަ௨େֶͷWeinan ZHANGॿڭत͕·ͱΊ͍ͯΔɺ ΦϯϥΠϯ޿ࠂؔ܎ͷ࿦จू • https://github.com/wnzhang/rtb-papers • δϟϯϧ͝ͱʹओཁͳ࿦จ͕·ͱ·͍ͬͯΔͷͰɺ޿ࠂܥ ͷػցֶशٕज़ͷೖ໳ʹ͓͢͢Ί • ࠓճ͸CTR/CVRਪఆʹؔ͢Δ࿦จΛ঺հ͠·͢ 

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 

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 

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 

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 

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๏ 

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 

Modeling Delayed Feedback in Display Advertising by Olivier Chapelle. KDD 2014. • "σΟεϓϨΠ޿ࠂʹ͓͚Δ஗ԆϑΟʔυόοΫϞσϦϯ ά" • Criteo • CV͸impޙ௕࣌ؒܦ͔ͬͯΒൃੜ͢Δ • ͜ͷ஗ԆΛߟྀͨ͠CV༧ଌϞσϧΛ࡞ͬͨ 

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) 

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࿩ 

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) 

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࿩୊΋ 

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 

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 

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 

A Convolutional Click Prediction Model by Qiang Liu, Feng Yu, Shu Wu, Liang Wang. CIKM 2015. • "৞ΈࠐΈΫϦοΫ༧ଌϞσϧ" • imp୯ମͰ͸ͳ͘ܥྻΛ࢖ͬͯCTR༧ଌ͢ΔϞσϧ • impͷܥྻʹରԠ͢Δಛ௃ྔΛೖྗɺ৞ΈࠐΈ͢Δ 

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/ 

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΋޷੒੷ 

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 

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 

Deep CTR Prediction in Display Advertising by Junxuan Chen et al. MM 2016. • "ਂ૚CTRਪఆ" • Alibaba • ඪ४తͳಛ௃ྔʹՃ͑ͯόφʔը૾Λ࢖༻ͨ͠CTRਪఆ 

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૷ 

Product-based Neural Networks for User Response Prediction by Yanru Qu et al. ICDM 2016. • "ProductϕʔεNNʹΑΔϢʔβԠ౴༧ଌ" • Product layerʹ͓͍ͯɺ಺ੵ/֎ੵΛ࢖ͬͯಛ௃ྔͷม׵Λ͍ͯ͠Δ 

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๏ 

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). 

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 

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 

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 

Deep Interest Network for Click-Through Rate Prediction by Guorui Zhou et al. ArXiv 2017. • "CTRਪఆͷͨΊͷDeep Interest Network" • Alibaba • ϢʔβͷߦಈཤྺΛݩʹCTRਪఆ͍ͨ͠ • ࣌ܥྻσʔλΛݩʹɺϢʔβͷࠓͷঢ়ଶΛϞσϦϯά͢Δ 

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 

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 

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 

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 

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 

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 

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/ 

·ͱΊ • CTR/CVR༧ଌ͸جຊతʹେن໛ͳςʔϒϧσʔλ • εέʔϥϏϦςΟ΍ަޓ࡞༻ͷදݱͳͲ͕ॏཁ • Ϟσϧ͸LR͕جຊɺަޓ࡞༻Λѻ͑ΔFM͕҆ఆ • ۙ೥͸ը૾΍࣌ܥྻσʔλɺΑΓෳࡶͳަޓ࡞༻Λѻ͏ͨ ΊDNNͷಋೖ΋