Evaluation of Tensor-Based Algorithms for Real-Time Bidding Optimization
[ 1 ] Instytut Automatyki i Inżynierii Informatycznej, Wydział Elektryczny, Politechnika Poznańska | [ P ] employee
2017
chapter in monograph / paper
english
- Big Data
- Context-aware recommendation
- Tensor decomposition
- Logistic regression
- Click-Through Rate prediction
- WWW
- Display advertising
- Real-Time Bidding
- Demand-Side Platform
EN In this paper we evaluate tensor-based approaches to the Real-Time Bidding (RTB) Click-Through Rate (CTR) estimation problem. We propose two new tensor-based CTR prediction algorithms. We analyze the evaluation results collected from several papers – obtained with the use of the iPinYou contest dataset and the Area Underneath the ROC curve measure. We accompany these results with analogical results of our experiments – conducted with the use of our implementations of tensor-based algorithms and approaches based on the logistic regression. In contrast to the results of other authors, we show that biases – in particular those being low-order expectation value estimates – are at least as useful as outcomes of high-order components’ processing. Moreover, on the basis of Average Precision results, we postulate that ROC curve should not be the only characteristic used to evaluate RTB CTR estimation performance.
26.02.2017
160 - 169
20
20
WoS (15)