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完整後設資料紀錄
DC 欄位 | 值 | 語言 |
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dc.contributor.advisor | 盧信銘(Hsin-Min Lu) | |
dc.contributor.author | You-Jing Liu | en |
dc.contributor.author | 劉幼菁 | zh_TW |
dc.date.accessioned | 2021-06-16T09:16:30Z | - |
dc.date.available | 2018-07-20 | |
dc.date.copyright | 2017-07-20 | |
dc.date.issued | 2017 | |
dc.date.submitted | 2017-07-14 | |
dc.identifier.citation | Aigner, D., Lovell, C. K., & Schmidt, P. (1977). Formulation and estimation of stochastic frontier production function models. Journal of Econometrics, 6(1), 21-37.
Balakrishnan, R., & Bhatt, R. P. (2015). Real-time bid optimization for group-buying ads. ACM Transactions on Intelligent Systems and Technology (TIST), 5(4), 62. Chen, Y., Berkhin, P., Anderson, B., & Devanur, N. R. (2011, August). Real-time bidding algorithms for performance-based display ad allocation. In Proceedings of the 17th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (pp. 1307-1315). ACM. Cui, Y., Zhang, R., Li, W., & Mao, J. (2011, August). Bid landscape forecasting in online ad exchange marketplace. In Proceedings of the 17th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (pp. 265-273). ACM. Edelman, B. (2010). The design of online advertising markets. Handbook of Market Design. Google. (2011). The arrival of real-time bidding. Greene, W. H. (2008). The econometric approach to efficiency analysis. The measurement of productive efficiency and productivity growth, 1, 92-250. Ghosh, A., Rubinstein, B. I., Vassilvitskii, S., & Zinkevich, M. (2009, April). Adaptive bidding for display advertising. In Proceedings of the 18th International Conference on World Wide Web (pp. 251-260). ACM. Kumbhakar, S. C., & Lovell, C. K. (2003). Stochastic frontier analysis. Cambridge University Press. Meeusen, W., & van den Broeck, J. (1977). Efficiency estimation from Cobb-Douglas production functions with composed error. International Economic Review, 435-444. Wu, W. C. H., Yeh, M. Y., & Chen, M. S. (2015). Predicting winning price in real time bidding with censored data. In Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (pp. 1305-1314). ACM. Yuan, Y., Wang, F., Li, J., & Qin, R. (2014, October). A survey on real time bidding advertising. In Service Operations and Logistics, and Informatics (SOLI), 2014 IEEE International Conference on (pp. 418-423). IEEE. Zhang, W., & Wang, J. (2015). Statistical arbitrage mining for display advertising. In Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (pp. 1465-1474). ACM. Zhang, W., Yuan, S., & Wang, J. (2014, August). Optimal real-time bidding for display advertising. In Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (pp. 1077-1086). ACM. | |
dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/59132 | - |
dc.description.abstract | 預測線上廣告即時競價的得標價對於需求方平台而言,是參與競標前重要的工作項目,因為得標價相當於得標者最終所需支付的成本。本篇論文研究得標價的預測,使得需求方平台能夠藉此在即時競價市場中訂定適當的出價策略。然而,要精準地預測得標價相當困難,因為需求方平台無法觀察到過去未得標之競標的最終得標價。因此,本篇論文中提出一個混合模型,其結合分別由已知得標價的歷史已得標之競標資訊以及僅知出價的歷史未得標之競標資訊所訓練的兩個線性模型。此混合模型利用已知出價的歷史未得標之競標資料,來補足過去對於未得標之競標的得標價資訊空缺。最後,實驗結果顯示本篇論文所提出的混合模型對於得標價預測的結果優於線性迴歸模型及隨機生產前緣模型。 | zh_TW |
dc.description.abstract | From the viewpoint of a Demand-Side Platform (DSP), forecasting the winning price is an important task before bidding an ad impression because the winning price is equivalent to the cost that a DSP must pay after winning a bid. This paper studies on how to predict the winning price for an ad impression so that a DSP can win the ad impression by offering a suitable bidding price in the Real-Time Bidding (RTB). However, it is difficult to accurately estimate winning price for a DSP because the winning price is unobserved when a DSP lost the bid. Therefore, we propose a mixture model that is composed of two regression models learning from winning bids with observable winning price and losing bids with observable bidding price, respectively. The mixture model takes advantage of observable bidding price of historical losing bids to reconstruct the missing distribution of winning price. Last, the results of experiments show that the proposed mixture model outperforms linear regression model and stochastic production frontier in terms of winning price prediction. | en |
dc.description.provenance | Made available in DSpace on 2021-06-16T09:16:30Z (GMT). No. of bitstreams: 1 ntu-106-R03725030-1.pdf: 963442 bytes, checksum: 15cc42186d5392e9783742085797be1f (MD5) Previous issue date: 2017 | en |
dc.description.tableofcontents | 誌謝 i
摘要 ii ABSTRACT iii TABLE OF CONTENTS iv LIST OF FIGURES vi LIST OF TABLES vii Chapter 1 Introduction 1 1.1. Background 1 1.2. Research Motivations and Objectives 3 Chapter 2 Related Work 6 Chapter 3 Research Model 8 3.1. Winning and Losing Bids 8 3.2. Modeling Winning Price of Winning Bids 9 3.3. Modeling Bidding Price of Losing Bids 10 3.4. The Mixture Model 12 3.5. Baseline Models 13 3.5.1. Linear Regression Model 13 3.5.2. Stochastic Production Frontier 13 Chapter 4 Experimental Design 15 4.1. Dataset 15 4.2. Feature Representation 16 4.3. Feature Selection 17 4.4. Bidding Price Simulation 18 4.5. Proportion of Losing Bids 20 4.6. Parameters Estimation 20 4.6.1. Linear Regression Model 20 4.6.2. The Mixture Model 21 4.6.3. Stochastic Production Frontier 21 4.7. Winning Price Prediction 22 4.8. Evaluation Metric 22 4.9. Experimental Procedure 23 Chapter 5 Results 24 Chapter 6 Conclusion 29 6.1. Conclusion 29 6.2. Future Work 29 References 30 Appendix 32 | |
dc.language.iso | en | |
dc.title | 線上廣告即時競價之得標價預測 | zh_TW |
dc.title | Winning Price Prediction in Real-Time Bidding for Online Advertising | en |
dc.type | Thesis | |
dc.date.schoolyear | 105-2 | |
dc.description.degree | 碩士 | |
dc.contributor.oralexamcommittee | 曹承礎(Seng-Cho Chou),陳文華(Wun-Hwa Chen) | |
dc.subject.keyword | 線上廣告,即時競價,需求方平台,得標價預測,展示型廣告, | zh_TW |
dc.subject.keyword | Online Advertising,Real-Time Bidding,Demand-Side Platform,Winning Price Prediction,Display Advertising, | en |
dc.relation.page | 42 | |
dc.identifier.doi | 10.6342/NTU201701505 | |
dc.rights.note | 有償授權 | |
dc.date.accepted | 2017-07-14 | |
dc.contributor.author-college | 管理學院 | zh_TW |
dc.contributor.author-dept | 資訊管理學研究所 | zh_TW |
顯示於系所單位: | 資訊管理學系 |
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