Abstract
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.
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This work is supported by the Polish National Science Centre, grant DEC-2011/01/D/ST6/06788.
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Szwabe, A., Misiorek, P., Ciesielczyk, M. (2017). Evaluation of Tensor-Based Algorithms for Real-Time Bidding Optimization. In: Nguyen, N., Tojo, S., Nguyen, L., Trawiński, B. (eds) Intelligent Information and Database Systems. ACIIDS 2017. Lecture Notes in Computer Science(), vol 10191. Springer, Cham. https://doi.org/10.1007/978-3-319-54472-4_16
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