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| DC 欄位 | 值 | 語言 |
|---|---|---|
| dc.contributor.advisor | 陳銘憲 | |
| dc.contributor.author | Wush Chi-Hsuan Wu | en |
| dc.contributor.author | 吳齊軒 | zh_TW |
| dc.date.accessioned | 2021-05-19T17:42:52Z | - |
| dc.date.available | 2024-01-30 | |
| dc.date.available | 2021-05-19T17:42:52Z | - |
| dc.date.copyright | 2019-01-30 | |
| dc.date.issued | 2019 | |
| dc.date.submitted | 2019-01-29 | |
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| dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/7399 | - |
| dc.description.abstract | Real-Time Bidding is currently the most popular ad auction process for online advertising.
In this study, we study how to predict the winning price of each bid from the aspect of a bidder by leveraging the machine learning and statistical methods on the bidding history. A major challenge is that the real winning price is not observed by the bidder after losing. We propose to utilize the idea from censored regression model, which is widely used in the survival analysis and econometrics, to derive the loss for the losing data. Moreover, the assumption of the censored regression is violated in the real data, so we propose a model which uses the winning rate prediction to mitigate the impact of violation. It is named as the mixture model. Furthermore, We generalize the winning price model to incorporate the deep learning models with different distributions and propose an algorithm to learn from the historical bidding information, where the winning price are either observed or partially observed. We study if the successful deep learning models of the click-through rate can enhance the prediction of the winning price or not. We also study how different distributions of winning price can affect the learning results. Experiment results show that the censored regression usually outperforms the linear regression and the proposed averaged model always outperforms the linear regression. Experiment results also show that the deep learning models indeed boost the prediction quality when they are learned on the historical observed data. In addition, the deep learning models on the unobserved data are improved after learning from the censored data. Finally, we study the combination of the mixture model and the deep learning model. | en |
| dc.description.provenance | Made available in DSpace on 2021-05-19T17:42:52Z (GMT). No. of bitstreams: 1 ntu-108-D01921016-1.pdf: 695653 bytes, checksum: e496ff61f2980692be8114c1db38d37a (MD5) Previous issue date: 2019 | en |
| dc.description.tableofcontents | 口試委員會審定書iii
誌謝v 摘要vii Abstract ix 1 Introduction 1 2 The Winning Price of the RTB Display Advertising 5 3 Related Work 9 4 The Models of the Winning Price 11 4.1 Winning Price Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11 4.2 Generalized Winning Price Model . . . . . . . . . . . . . . . . . . . . . 13 4.2.1 Link Structures . . . . . . . . . . . . . . . . . . . . . . . . . . . 14 4.2.2 Distributions . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15 4.3 Mixture Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17 5 Algorithm 23 6 Experiments 27 6.1 Datasets and Features . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27 6.2 The Difference Between the Winning Data and Losing Data . . . . . . . 33 xi 6.3 The Performance of the Censored Regression . . . . . . . . . . . . . . . 34 6.4 The Performance of the Mixture Model . . . . . . . . . . . . . . . . . . 34 6.5 Comparing Different Link Structures on the Winning Data . . . . . . . . 35 6.6 Comparing Different Distributions . . . . . . . . . . . . . . . . . . . . . 37 6.7 Overall Comparison of Link Structures and Distributions . . . . . . . . . 37 6.8 The Mixture Model of Generalized Winning Price Model . . . . . . . . . 38 7 Conclusion and Future Work 43 Bibliography 45 | |
| dc.language.iso | en | |
| dc.title | 在即時競標中使用設限資料預測可獲勝的價格 | zh_TW |
| dc.title | Predicting Winning Price in Real Time Bidding with Censored Data | en |
| dc.type | Thesis | |
| dc.date.schoolyear | 107-1 | |
| dc.description.degree | 博士 | |
| dc.contributor.oralexamcommittee | 廖弘源,曾新穆,駱明凌,林軒田,葉彌妍 | |
| dc.subject.keyword | 網路廣告,即時競標,機器學習,深度學習, | zh_TW |
| dc.subject.keyword | Online Advertising,Real-Time Bidding,Machine Learning,Deep Learning, | en |
| dc.relation.page | 48 | |
| dc.identifier.doi | 10.6342/NTU201900270 | |
| dc.rights.note | 同意授權(全球公開) | |
| dc.date.accepted | 2019-01-29 | |
| dc.contributor.author-college | 電機資訊學院 | zh_TW |
| dc.contributor.author-dept | 電機工程學研究所 | zh_TW |
| dc.date.embargo-lift | 2024-01-30 | - |
| 顯示於系所單位： | 電機工程學系 | |
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