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Attribution of Customers’ Actions Based on Machine Learning Approach
P. 77–88.
Timur Kadyrov, Ignatov D. I.
A multichannel attribution model based on gradient boost- ing over trees is proposed, which was compared with the state of the art models: bagged logistic regression, Markov chains approach, shapely value. Experiments on digital advertising datasets showed that the pro- posed model is better than the solutions considered by ROC AUC metric. In addition, the problem of probability prediction of conversion by the consumer using the ensemble of the analyzed algorithms was solved, the meta-features obtained were enriched with consumers and offline activities of the advertising campaign data.
Keywords: цифровая рекламаGradient boostingградиентный бустингMulti-touch attributionDigital advertisingData-driven marketingМногоканальная атрибуция рекламымаркетинг на основе данных
Publication based on the results of:
In book
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Nikita I. Lysenok, International Journal of Computer Information Systems and Industrial Management Applications 2026 Vol. 18 No. 17s P. 533–549
This paper asks which families of volatility forecasting models create economic value in active trading, and through which integration channel that value is transmitted. Seven models drawn from four families — econometric (GJR-GARCH, HAR-J), gradient boosting (XGBoost, LightGBM), recurrent neural networks (LSTM, GRU) and a hybrid combining HAR-J with boosting — are compared on the ...
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advertising price set by three largest digital platforms — Google, YouTube and Facebook, — contributes to the explanations of the roots of platforms market power. During the ...
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Zelenkov Y., , in: 2023 Ivannikov ISPRAS Open Conference (ISPRAS).: IEEE, 2023. P. 176–182.
We propose a regression ensemble based on a decomposition that separates the weighted average errors of individual learners and the ambiguity of their estimates. This approach is a modification of Gradient Boosting with a variation of the gradient at each step. That allows ensuring explicitly a diversity of base estimators. In addition, the proposed approach ...
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Avdasheva S. B., Chesnokov V., Тимофеева А. О., Финансы и бизнес 2023 Т. 19 № 4 С. 26–47
The study tests the hypothesis of a positive correlation between advertising expenditures and the concentration of social media and search engine users along with other determinants, as well as a comparison of digital advertising expenditures in Russia and other countries based on panel analysis. The effect of digital platform user concentration was assessed for the ...
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Решающие деревья широко применяются в машинном обуче
нии, статистике и анализе данных. Предиктивные модели, осно
ванные на решающих деревьях, показывают отличные результаты
в терминах точности и времени обучения, особенно на гетерогенных
табличных датасетах. Производительность, простота и надежность
делают это семейство алгоритмов одним из наиболее популярных
в машинном обучении и науке о данных.
Одним из важных гиперпараметров алгоритмов, основанных на
решающих деревьях, является максимальная ...
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Muratova A., Mitrofanova E., Islam R., , in: Procedia Computer Science: 11th International Young Scientist Conference on Computational ScienceVol. 212.: Elsevier, 2022. P. 358–367.
The article presents a case study on demographic sequences analysis through modern machine learning (ML)
techniques. The studied data contains demographic and socioeconomic events, where the events are presented
as sequences of statuses. The involved demographers are interested in applications of advanced ML techniques
and interpretable patterns for their needs. We show how Shapley value-based explanations can be ...
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Shurmina I., Interactive Entertainment Law Review 2020 No. 3 P. 59–66
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This paper introduces Stochastic Gradient Langevin Boosting (SGLB) - a powerful and efficient machine learning framework that may deal with a wide range of loss functions and has provable generalization guarantees. The method is based on a special form of the Langevin diffusion equation specifically designed for gradient boosting. This allows us to theoretically guarantee ...
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Liudmila Prokhorenkova, Ustimenko A., , in: International Conference on Machine Learning (ICML 2020)Vol. 119.: PMLR, 2020. P. 9669–9679.
In this paper, we introduce a powerful and efficient framework for direct optimization of ranking metrics. The problem is ill-posed due to the discrete structure of the loss, and to deal with that, we introduce two important techniques: stochastic smoothing and novel gradient estimate based on partial integration. We show that classic smoothing approaches may ...
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Antipov E. A., Pokryshevskaya E. B., Journal of Revenue and Pricing Management 2020 No. 19 P. 355–364
Forecasting demand and understanding sales drivers are one of the most important tasks in retail analytics. However, traditionally, linear models and/or models with a small number of predictors have been predominantly used in sales modeling. Taking into account that real-world demand is naturally determined by complex substitution and complementation patterns among a large number of ...
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Added: May 1, 2020
Alexandrovskiy S., Trundova Olga, International Journal of Internet Marketing and Advertising 2022 Vol. 16 No. 1/2 P. 19–37
The authors aim to offer a probability approach for measuring media contribution to online conversions in e-commerce. The authors reviewed literature on attribution modelling with application of heuristics (Google Analytics) and probability (Markov chains) models. The survey used the data of customer journeys from 134132 users to build up the attribution model. As a result, ...
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Kuralenok I., Ershov V., Лабутин И. Н., , in: Advances in Neural Information Processing Systems 32 (NeurIPS 2019).: [б.и.], 2019. P. 1–10.
In this work, we introduce a new decision tree ensemble representation framework: instead of using a graph model we transform each tree into a well-known polynomial form. We apply the new representation to three tasks: theoretical analysis, model reduction, and interpretation. The polynomial form of a tree ensemble allows a straightforward interpretation of the original ...
Added: December 27, 2019