Andrzej Szwabe
ABSTRACT
In this paper we investigate one of the most interesting problems of Big Data user feedback prediction which is the Real-Time Bidding Click-Through Rate estimation. We evaluate experimentally the impact of the widely-referenced methods for optimization of the logistic regression - the state-of-the art Real-Time Bidding optimization method - on the quality of CTR estimation. From the perspective of this impact, we evaluate different configurations of widely-referenced regularization techniques and compare them with a simple technique of the feature generalization. On the basis of the results of the extensive experimentation, we show that in the context of the application scenario investigated herein, an optimization of the stochastic gradient descent algorithm configuration may be successfully accompanied, or even replaced, by a simple feature generalization.
- Provost, F., and Fawcett, T. Data Science and its Relationship to Big Data and Data-Driven Decision Making. Big Data 1 (2013), 51--9.Google Scholar
Cross Ref
- Chapelle, O., Manavoglu, E., and Rosales, R. 2014. Simple and Scalable Response Prediction for Display Advertising. ACM Transactions on Intelligent Systems and Technology 5, 4,61:1--61:34. Google ScholarDigital Library
- Lee, K., Orten, B., Dasdan, A., and Li, W. 2012. Estimating Conversion Rate in Display Advertising from Past Performance Data. In Proceedings of the 18th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (New York, NY, USA, 2012), KDD '12, ACM, pp. 768--776. Google ScholarDigital Library
- Zhang, W., Yuan, S., and Wang, J. 2014. Real-time bidding benchmarking with iPinYou dataset. CoRR abs/1407.7073, 1--10.Google Scholar
- Zhang, W., Yuan, S., and Wang, J. 2014. Optimal Real-time Bidding for Display Advertising. In Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (New York, NY, USA, 2014), KDD '14, ACM, pp. 1077--1086. Google ScholarDigital Library
- Shan, L., Lin, L., Sun, C., and Wang, X. 2016. Predicting Ad Click-Through Rates via Feature-Based Fully Coupled Interaction Tensor Factorization. Electronic Commerce Research and Applications 16, 30--42. Google ScholarDigital Library
- Wang, J., and Yuan, S. Real-Time Bidding: A New Frontier of Computational Advertising Research. 2015. In Proceedings of the Eighth ACM International Conference on Web Search and Data Mining (New York, NY, USA, 2015), WSDM'15, ACM, pp. 415--416. Google ScholarDigital Library
- Zhang, W., Du, T., and Wang, J. 2016. Deep Learning over Multi-Field Categorical Data. Springer International Publishing, Cham, pp. 45--57.Google Scholar
- Shani, G., and Gunawardana, A. 2011. Evaluating recommendation systems. In Recommender Systems Handbook, F. Ricci, L. Rokach, B. Shapira, and P. B. Kantor, Eds. Springer US, pp. 257--297.Google Scholar
- Fawcett, T. 2006. An Introduction to ROC Analysis. Pattern Recogn. Lett. 27, 8, 861--874. Google ScholarDigital Library
- Ren, K., Zhang, W., Rong, Y., Zhang, H., Yu, Y., and Wang, J. 2016. User response learning for directly optimizing campaign performance in display advertising. In Proceedings of the 25th ACM International on Conference on Information and Knowledge Management (New York, NY, USA, 2016), CIKM '16, ACM, pp. 679--688. Google ScholarDigital Library
- Bottou, L. 2012. Stochastic gradient tricks. In Neural Networks, Tricks of the Trade, Reloaded, vol. 7700. Springer, p. 430--445.Google Scholar
- Richardson, M., Dominowska, E., and Ragno, R. 2007. Predicting clicks: Estimating the click-through rate for new ads. In Proceedings of the 16th International Conference on World Wide Web (New York, NY, USA, 2007), WWW '07, ACM, pp. 521--530. Google ScholarDigital Library
- Ciesielczyk, M., Szwabe, A., Morzy, M., and Misiorek, P. 2017. Progressive Random Indexing: Dimensionality Reduction Preserving Local Network Dependencies. ACM Transactions on Internet Technology (in press). Google ScholarDigital Library
- Davis, J., and Goadrich, M. 2006. The Relationship Between Precision-Recall and ROC Curves. In Proceedings of the 23rd International Conference on Machine Learning (New York, NY, USA, 2006), ICML '06, ACM, pp. 233--240. Google ScholarDigital Library
- Wu, W. C.-H., Yeh, M.-Y., and 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 (New York, NY, USA, 2015), KDD '15, ACM, pp. 1305--1314. Google ScholarDigital Library
- Liao, H., Peng, L., Liu, Z., and Shen, X. 2014. iPinYou Global RTB Bidding Algorithm Competition Dataset. In Proceedings of the Eighth International Workshop on Data Mining for Online Advertising (New York, NY, USA, 2014), ADKDD'14, ACM, pp. 6:1--6:6. Google ScholarDigital Library
- He, X., Pan, J., Jin, O., Xu, T., Liu, B., Xu, T., Shi, Y., Atallah, A., Herbrich, R., Bowers, S., and Candela, J. Q. 2014. Practical Lessons from Predicting Clicks on Ads at Facebook. In Proceedings of the Eighth International Workshop on Data Mining for Online Advertising (New York, NY, USA, 2014), ADKDD'14, ACM, pp. 5:1--5:9. Google ScholarDigital Library
- DBpedia. 2016. The DBpedia Knowledge Base. http://wiki.dbpedia.org/Google Scholar
Recommendations
Optimal real-time bidding for display advertising
KDD '14: Proceedings of the 20th ACM SIGKDD international conference on Knowledge discovery and data miningIn this paper we study bid optimisation for real-time bidding (RTB) based display advertising. RTB allows advertisers to bid on a display ad impression in real time when it is being generated. It goes beyond contextual advertising by motivating the ...
Feedback Control of Real-Time Display Advertising
WSDM '16: Proceedings of the Ninth ACM International Conference on Web Search and Data MiningReal-Time Bidding (RTB) is revolutionising display advertising by facilitating per-impression auctions to buy ad impressions as they are being generated. Being able to use impression-level data, such as user cookies, encourages user behaviour targeting, ...
Combining Powers of Two Predictors in Optimizing Real-Time Bidding Strategy under Constrained Budget
CIKM '16: Proceedings of the 25th ACM International on Conference on Information and Knowledge ManagementWe address the bidding strategy design problem faced by a Demand-Side Platform (DSP) in Real-Time Bidding (RTB) advertising. A RTB campaign consists of various parameters and usually a predefined budget. Under the budget constraint of a campaign, ...
Comments