How machine learning helps in uplift modelling?
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Machine learning helps in uplift modeling by utilizing data analysis techniques to measure the incremental effect, or uplift, of a strategy or action on users. Traditional uplift methods often rely on individual data, which may not capture all the hidden factors related to uplift. To address this, a graph neural network-based framework called GNUM has been proposed, which leverages the features of neighbors and social relationships to estimate uplift . Uplift modeling has been particularly effective in churn prediction for telecom customers, where it helps reduce churn by identifying customers who are more likely to be retained . In the field of engineering, machine learning techniques such as gradient-boosting decision trees (GBDT) and particle swarm optimization (PSO) have been used to predict the uplift behavior of helical anchors in sand, providing valuable insights for offshore wind energy harvesting . Additionally, machine learning algorithms have been applied to uplift modeling in marketing, advertising, and product personalization experiments, where feature selection methods based on causal inference have been introduced to improve performance and interpretability .
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| Papers (4) | Insight |
|---|---|
| Machine learning, specifically the gradient-boosting decision trees (GBDT) algorithm, is used in the paper to construct a prediction model for the uplift behavior of helical anchors in dense sand for wind energy harvesting. | |
30 Apr 2023 | The paper proposes a graph neural network-based framework with two uplift estimators to learn from the social graph for uplift estimation. Machine learning is used to model the treatment and control group data together and to utilize more labeled data to alleviate the label scarcity problem. |
15 Dec 2022 | The paper discusses how uplift modeling, a prescriptive analytic machine learning technique, can be used in churn prediction for telecom customers to reduce churn and improve customer relationship management. |
15 Dec 2022 | The paper discusses how uplift modeling, a prescriptive analytic machine learning technique, can be used in churn prediction for telecom customers to reduce churn and improve customer relationship management. |
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