Browse ORBilu by Open Access
- What it is and what it isn't
- Green Road / Gold Road?
- Ready to Publish. Now What?
- How can I support the OA movement?
- Where can I learn more?
ORBilu is a project developed by
  Towards Optimal Real-Time Bidding Strategies for Display AdvertisingDu, Manxing  Doctoral thesis (2019) Detailed reference viewed: 126 (11 UL)  Time Series Modeling of Market Price in Real-Time BiddingDu, Manxing ; Hammerschmidt, Christian ; Varisteas, Georgios et alin 27th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (2019, April) Real-Time-Bidding (RTB) is one of the most popular online advertisement selling mechanisms. Modeling the highly dynamic bidding environment is crucial for making good bids. Market prices of auctions ... [more ▼] Real-Time-Bidding (RTB) is one of the most popular online advertisement selling mechanisms. Modeling the highly dynamic bidding environment is crucial for making good bids. Market prices of auctions fluctuate heavily within short time spans. State-of-the-art methods neglect the temporal dependencies of bidders’ behaviors. In this paper, the bid requests are aggregated by time and the mean market price per aggregated segment is modeled as a time series. We show that the Long Short Term Memory (LSTM) neural network outperforms the state-of-the-art univariate time series models by capturing the nonlinear temporal dependencies in the market price. We further improve the predicting performance by adding a summary of exogenous features from bid requests. [less ▲] Detailed reference viewed: 177 (17 UL)Know Your Enemies and Know Yourself in the Real-Time Bidding Function OptimisationDu, Manxing ; ; et alin Proceedings of the 19th IEEE International Conference on Data Mining Workshops (ICDMW 2019) (2019) Detailed reference viewed: 411 (5 UL)Improving Real-Time Bidding Using a Constrained Markov Decision ProcessDu, Manxing ; Sassioui, Redouane ; Varisteas, Georgios et alin Proceedings of the 13th International Conference on Advanced Data Mining and Applications (2017, November) Detailed reference viewed: 292 (18 UL)The Top 10 Topics in Machine Learning Revisited: A Quantitative Meta-StudyGlauner, Patrick ; Du, Manxing ; et alin Proceedings of the 25th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (ESANN 2017) (2017) Which topics of machine learning are most commonly addressed in research? This question was initially answered in 2007 by doing a qualitative survey among distinguished researchers. In our study, we ... [more ▼] Which topics of machine learning are most commonly addressed in research? This question was initially answered in 2007 by doing a qualitative survey among distinguished researchers. In our study, we revisit this question from a quantitative perspective. Concretely, we collect 54K abstracts of papers published between 2007 and 2016 in leading machine learning journals and conferences. We then use machine learning in order to determine the top 10 topics in machine learning. We not only include models, but provide a holistic view across optimization, data, features, etc. This quantitative approach allows reducing the bias of surveys. It reveals new and up-to-date insights into what the 10 most prolific topics in machine learning research are. This allows researchers to identify popular topics as well as new and rising topics for their research. [less ▲] Detailed reference viewed: 209 (25 UL)Behavior Profiling for Mobile AdvertisingDu, Manxing ; State, Radu ; et alin Proceedings of the 3rd IEEE/ACM International Conference on Big Data Computing, Applications and Technologies (2016, December) Detailed reference viewed: 202 (20 UL) |
||
