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ï ä»å¹Žã®WSDMã§çºè¡šãããææ°ã®åºåã³ã³
ããŒãžã§ã³æé©åã®ææ³ã«ã€ããŠçºè¡šããŸ
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8. ã¿ãŒã²ãã£ã³ã°åºå
ï åºåã衚瀺ããéã«äžå®ã®å¯Ÿè±¡ã®ã¿ã«åºå
ã衚瀺ãã
ï äŸãã°ç·æ§ã®ã¿ã«è¡šç€ºãæ±äº¬ã«äœãã§ãã
人ã®ã¿ãè»ã«èå³ã®ãã人ã察象ã®åºåã«
èå³ã®ãã人ãªã©
ï ãããè¡ãããã«ã¯ãµã€ãã蚪åãããŠãŒ
ã¶ã®å±æ§ãèå³ãæšå®ããå¿
èŠããã
æ©æ¢°åŠç¿ãªã©ã®ææ³ã«ããããŠãŒ
ã¶ã®è¡åå±¥æŽããŒã¿ã«åºã¥ããŠå±
æ§ãèå³ãæšå®ãã
10. Background :
Computational advertising
ï Yahoo! Research, Microsoft Researchãªã©
ãäžå¿ã«ãªã³ã©ã€ã³åºåã«æ©æ¢°åŠç¿ãæ
å ±
æ€çŽ¢ãªã©ã®æè¡ãé©çšãããšããç 究ãè¡
ãããŠãã
ï http://www.stanford.edu/class/msande239/
ï Yahoo! Researchã«é¢ããŠã¯äž»èŠãªç 究è
ãä»å¹Ž
ã«å
¥ã£ãŠå€§åMicrosoft, Googleã«ç§»åããŠãã
11. èå³ã®æšå®
ï ã²ãŒã ãªã©ã«ããŽãªããŒã¹ã§æšå®ããæ¹æ³
ï Large-scale behavioral targeting, KDD 2009
www.google.com/ads/preferences/ ãã
12. èå³ã®æšå®
ï ãã®åºåãã£ã³ããŒã³ã«å¯ŸããŠã³ã³ããŒ
ãžã§ã³ãããŠãŒã¶ãããšã«è¿ããŠãŒã¶ã
ã¿ãŒã²ãããšãã
13. åŸæ¥ç 究
ï ã¯ãªãã¯ãæ倧åãããã®
ï Large-scale behavioral targeting, KDD 2009
ï How much can behavioral targeting help online advertising,
WWW 2009
ï Learning relevance from a heterogeneous social network
and its application in online targeting, SIGIR 2011
ï ã³ã³ããŒãžã§ã³ãæ倧åãããã®
ï Large-scale customized models for advertisers, ICDM
2010
ï Learning to Target: What Works for Behavioral Targeting,
CIKM 2011
14. 玹ä»è«æ
ï Finding the right consumer : Optimizing for
conversion in display advertising campaigns
ï Yandong Liu(Carnegie Mellon), Sandeep Pandey,
Deepak Agarwal, Vanja Josifovski(Yahoo!
Research)
ï ãŠãŒã¶ã®éå»ã®è¡åå±¥æŽããã³ã³ããŒãžã§
ã³ãèµ·ãããããªãŠãŒã¶ãçºèŠãã
ï ã³ã³ããŒãžã§ã³ãèµ·ãããããªãŠãŒã¶ãçº
èŠããããšã«ãããé©åãªãŠãŒã¶ã«å¯ŸããŠ
åºåãå±ããããšãã§ãã
16. æ¬ç 究ã®ææ
ï æ¬ç 究ã§ã¯åºåãã£ã³ããŒã³ããšã®local
modelã«å ããŠãä»ã®ãã£ã³ããŒã³ã®æ
å ±ã
çšããglobal modelãçšããããšã«ãããã³
ã³ããŒãžã§ã³ã®æšå®ç²ŸåºŠãåäžãã
17. Notation
ï ð¥ ð¢ â ð
ð : ãŠãŒã¶ð¢ãè¡šããã¯ãã«
ï ð§ ð â ð
ð : ãã£ã³ããŒã³ðãè¡šããã¯ãã«
ï ð(ð¥ ð¢ , ð§ ð , ð) : ãŠãŒã¶ð¢ããã£ã³ããŒã³ðã«é¢ã
ãŠã³ã³ããŒãžã§ã³ããåŸå
ï ð(ð¥ ð¢ , ð§ ð , ð)ãåŠç¿ããã®ããã®è«æã§ã®èª²é¡
18. User representation
ï ã¯ãšãªãããŒãžé²èŠ§ãåºåã¯ãªãã¯ãªã©ã
ããã¹ãã«å€æããŠBOWè¡šçŸããã
ï ãã ãé »åºŠæ
å ±ã¯ç¡èŠããŠ0/1ã§è¡šã
19. Campaign representation
ï åºåãã£ã³ããŒã³ã¯2ã€ã®èŠçŽ ããæ§æãã
ã
ï åºåã®ã©ã³ãã£ã³ã°ããŒãž(ã¡ã¿ããŒã¿)
ï ãã£ã³ããŒã³ã§ã³ã³ããŒãžã§ã³ãããŠãŒã¶çŸ€
20. Modeling approaches
ï ð ð¥ ð¢ , ð§ ð , ð = ð ð¥ ð¢ , ð§ ð + ðð (ð¥ ð¢ )
ï ãšå解ãã
ï ðã¯ãã£ã³ããŒã³ã®ã¡ã¿ããŒã¿ã«ãããããªãé¢
æ°ã§ãã
ï ðã¯ãã£ã³ããŒã³ðã«åºæã®å€ã§ãã
ï ðã®åŠç¿æ³ãšããŠã¯ä»¥äžã®3ã€ãèãããã
ï Linear SVM
ï Logistic regression
ï Naive Bayes
21. Local model using seed sets
ï ð ð¥ ð¢ , ð§ ð , ð = ðð (ð¥ ð¢ )ã®å Žåãèãã
ï ããã¯ãã£ã³ããŒã³ã®ã¡ã¿æ
å ±ã䜿ããã«ã
ãã£ã³ããŒã³ðã«å¯ŸããŠã³ã³ããŒãžã§ã³ãã
ãŠãŒã¶ãšããªãã£ããŠãŒã¶ã䜿ã£ãŠåŠç¿ã
ãããšã«çžåœãã
ï SVM, Logistic regressionã®å Žåã¯
ð
ï ðð ð¥ ð¢ = ð¥ ð¢ ðœãšãªãããã®ðœãåŠç¿ãã
22. Global model using the campaign
metadata
ï ãã£ã³ããŒã³ã®ã©ã³ãã£ã³ã°ããŒãžãªã©ã®
ã¡ã¿æ
å ±ã䜿ã£ãŠãæé©åãè¡ã
ï ææ³ãšããŠã¯ä»¥äžã®2ã€ãèãã
ï Merge-based global model
ï Interaction-based global model
23. Merge-based global model
ï ð ð¥ ð¢ , ð§ ð , ð = ð¥ â²ð¢ ðœãšã¢ãã«åãã
ï ãã£ã³ããŒã³ããšã®å·®ç°ãç¡èŠããŠãäžè¬
çã«ã³ã³ããŒãžã§ã³ãããããŠãŒã¶ãéžæ
ããããšã«ãªã
24. Interaction-based global model
ï ð ð¥ ð¢ , ð§ ð , ð = ð¥ â²ð¢ ð·ð§ ð + ð¥ â²ð¢ ðœãšã¢ãã«åãã
ï ããã§è¡åð·ã¯ð à ðè¡åã§ãŠãŒã¶ç¹åŸŽéãš
ãã£ã³ããŒã³ç¹åŸŽééã®éã¿ãè¡šã
ï ãã®ãŸãŸã§ã¯ðã倧ããããã®ã§å€æ°éžæãã
ï ð ðð ãç¹åŸŽéðãæã£ããŠãŒã¶ããã£ã³ããŒã³ðã«ã³ã³
ããŒãžã§ã³ãã確çãšãã
ï ð ð. ãç¹åŸŽéðãæã£ããŠãŒã¶ãã³ã³ããŒãžã§ã³ãã確
çãšãã
ð
ï KLãã€ããŒãžã§ã³ã¹ ð ð ðð log ðð ã®äžäœãéžæãã
ð ð.
25. Global + Local model
ï Interaction-based global modelãšLocal modelã
åããã
ï åŠç¿æ³ãšããŠã¯
ï ð ð = ðãšããŠåæåŠç¿ãè¡ã
ï åãã«global modelãåŠç¿ããŠãåå¥ã«local modelã
åŠç¿ãã
ï ã®2ã€ãèãããã
26. Experiments
ï 2011幎ã®Adnetworkããã©ã³ãã ã«éžãã
10åã®ãã£ã³ããŒã³ãå©çš
ï ã³ã³ããŒãžã§ã³ã®äºæž¬å¯Ÿè±¡ãšãªã£ããŠãŒã¶
ã¯300,000以äž
ï ã³ã³ããŒãžã§ã³ããªãã£ããŠãŒã¶ã¯ãã
ãŠãŒã¶ã«æ¯ã¹ãŠéåžžã«å€ãã®ã§ãåãã£ã³
ããŒã³ã«ã€ãã©ã³ãã ã«30000ãŠãŒã¶ãéžæ
ããŠè² äŸãšãã
28. åŠç¿ã¢ã«ãŽãªãºã ã«ããéã
ï Local modelã«é¢ããŠ3ã€ã®åŠç¿ã¢ã«ãŽãªãºã ã®æ¯èŒã
è¡ã£ã
ï SVMãšLogisticã¯ã»ãŒåãæ§èœãNaive-Bayesã¯ããŸãã
ããªã
ï ãã®åŸã®å®éšã§ã¯SVMãå©çšãã
29. åŠç¿åšã®Sensibility
ï SVM, Logisticã¯æ£ååå®æ°ã«ãã£ãŠç²ŸåºŠã
倧ããå€ãã
ï Naive Bayesã®æ¹ã¯ããã«æ¯ã¹ãŠRobust
30. ããŒã¿ãµã€ãºãšç²ŸåºŠã®é¢ä¿
ï åäžãµã€ãºã®ãã£ã³ããŒã³ã«ãããŠã¯ããŒã¿ã
å¢ããã»ã©ç²ŸåºŠãé«ããªã
ï Smallãã£ã³ããŒã³ã®æ¹ãLargeãã£ã³ããŒã³ãã粟
床ãé«ãã®ã¯Smallã®æ¹ãã³ã³ããŒãžã§ã³ã®å®çŸ©ãå
åã泚æãããªã©å³æ Œã§ããLargeã«æ¯ã¹ãŠãã€ãºã
å°ãªããã
31. Global model
ï Medium, Largeãµã€ãºã®ãã£ã³ããŒã³ã«ãããŠã¯
ããŒã¿ãå°ãªããšãã«ã¯mergeã¢ãã«ã®æ¹ãé«ã粟
床ãšãªã£ã
ï smallã«é¢ããŠã¯ããŒã¿ãå°ãªãæãLocalã®æ¹ã粟床ãé«
ã
ï ãã æ¢åã®ãã£ã³ããŒã³ã®ã³ã³ããŒãžã§ã³ããŒã¿ãããŒ
ã¿ããªããšãã«å©çšããããšã«ãã£ãŠåæã®cold-startå
é¡ãé²ãã
33. Interaction-based global model
ï ãŠãŒã¶ã®ç¹åŸŽéã¯ç¹åŸŽéžæã«ãã3000ã«çµã£
ã
ï ãã£ã³ããŒã³ã®æ¹ã¯ãã£ã³ããŒã³ããšã«50åã
ããªãã®ã§ç¹ã«ç¹åŸŽéžæã¯è¡ããªãã£ã
ï ãããã®ãµã€ãºã«ãããŠãInteraction-based
modelã®æ¹ãé«ã粟床ã«ãªã£ã
34. Global + Local ã¢ãã«
ï Small,Largeã®ãã£ã³ããŒã³ã«ãããŠGlobal
+ Localã¢ãã«ã®æ¹ãGlobalã¢ãã«ãããé«
ã粟床ãšãªã£ã
35. ãŸãšã
ï æ¬ç 究ã§ã¯åºåãã£ã³ããŒã³ã®ã©ã³ãã£ã³
ã°ããŒãžãªã©ã®ã¡ã¿æ
å ±ã䜿ãããšã«ããã
åºåãã£ã³ããŒã³ã®ã³ã³ããŒãžã§ã³ããŒã¿
ããªããšãã«ãæå¹ãªã¢ãã«ãææ¡ãã
ï ä»åã®ç 究ã¯åºåã«æ³šåãããããã®ææ³
ã¯ã³ã³ãã³ãæšèŠãæ€çŽ¢ã®ããŒãœãã©ã€ãº
ãªã©ã«å©çšã§ãããšèãããã
36. ãã®ä»åºåã«é¢ãã話é¡
(æ€çŽ¢é£åååºå)
ï æ€çŽ¢åèªã«å¯ŸããŠãå
¥æãã
ãåºåã衚瀺ãã
ï æ€çŽ¢ãšã³ãžã³åŽã®æåŸ
åçãš
ããŠã¯(æåŸ
CTR) * (bidäŸ¡æ Œ)ãš
ãªã
ï åçãé«ããããCTRã®äºæž¬
ãé«ã粟床ã§è¡ãå¿
èŠããã
ï åºåã®è¡šç€ºäœçœ®ãåæã«è¡šç€ºãã
ãŠããç©å士ã®é¢ä¿ãèæ
®ããã¯
ãªãã¯ã¢ãã«ã®æ§ç¯ãå¿
èŠ
ï Relational click prediction for
sponsored search, WSDM 2012
ï Web-scale bayesian click-through
rate prediction for sponsored
search, ICML 2011
38. ãã®ä»åºåã«é¢ãã話é¡
ï ãŠãŒã¶ã«å¯ŸããŠåºåãé
ä¿¡ããéã«1impsã«ã
ãããŸã§æ¯æã£ãŠãããã決å®ããŠããªãã¹ã
åçãå€ããªãããã«ãã
ï Real-time bidding algorithms for performance-based
display ad allocation, KDD 2011
ï è€æ°ã®ã¢ããããã¯ãŒã¯ããã³æ€çŽ¢é£åååºå
ãªã©ã«å¯ŸããŠåºåãé
ä¿¡ããæã«ãååªäœãã³
ã³ããŒãžã§ã³ã«ã©ã®çšåºŠå¯äžããããããŒã¿ã
ãåæãã
ï Data-driven multi-touch attribution models, KDD 2011