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

Transcript

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

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2. 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·͢
   

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3. 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
   

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4. 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
   

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5. 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
   

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6. 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
   

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7. 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๏
   

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8. 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
   

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

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10. 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)
    

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11. 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࿩
    

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12. 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)
    

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13. 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࿩୊΋
    

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14. 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
    

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15. 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
    

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16. 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
    

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

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18. 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/
    

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19. 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΋޷੒੷
    

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20. 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
    

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21. 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
    

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

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23. 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૷
    

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

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25. 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๏
    

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26. 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).
    

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27. 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
    

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28. 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
    

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29. 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
    

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

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31. 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
    

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32. 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
    

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33. 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
    

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34. 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
    

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35. 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
    

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36. 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
    

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37. 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/
    

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

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