GBDT4CTRVis: visual analytics of gradient boosting decision tree for advertisement click-through rate prediction

IF 1.7 4区计算机科学 Q3 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS Journal of Visualization Pub Date : 2024-03-29 DOI:10.1007/s12650-024-00984-0

Wenwen Gao, Shangsong Liu, Yi Zhou, Fengjie Wang, Feng Zhou, Min Zhu

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Abstract

Gradient boosting decision tree (GBDT) is a mainstream model for advertisement click-through rate (CTR) prediction. Since the complex working mechanism of GBDT, advertising analysts often fail to analyze the decision-making and the iterative evolution process of a large number of decision trees, as well as to understand the impact of different features on the prediction results, which makes the model tuning quite challenging. To address these challenges, we propose a visual analytics system, GBDT4CTRVis, which helps advertising analysts understand the working mechanism of GBDT and facilitate model tuning through intuitive and interactive views. Specifically, we propose instance-level views to hierarchically explore the prediction results of advertising data, feature-level views to analyze the importance of features and their correlations from various perspectives, and model-level views to investigate the structure of representative decision trees and the temporal evolution of information gain during model prediction. We also provide multi-view interactions and panel control for flexible exploration. Finally, we evaluate GBDT4CTRVis through three case studies and expert evaluations. Feedback from experts indicated the usefulness and effectiveness of GBDT4CTRVis in helping to understand the model mechanism and tune the model.

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GBDT4CTRVis：用于广告点击率预测的梯度提升决策树可视化分析技术

摘要梯度提升决策树（GBDT）是广告点击率（CTR）预测的主流模型。由于 GBDT 的工作机制复杂，广告分析师往往无法分析大量决策树的决策和迭代演化过程，也无法理解不同特征对预测结果的影响，这使得模型调优颇具挑战性。为了应对这些挑战，我们提出了一个可视化分析系统--GBDT4CTRVis，通过直观的交互式视图，帮助广告分析师理解 GBDT 的工作机制并促进模型调整。具体来说，我们提出了实例级视图来分层探索广告数据的预测结果，提出了特征级视图来从不同角度分析特征的重要性及其相关性，还提出了模型级视图来研究代表性决策树的结构以及模型预测过程中信息增益的时间演化。我们还提供了多视图交互和面板控制，以便灵活探索。最后，我们通过三个案例研究和专家评估对 GBDT4CTRVis 进行了评估。专家的反馈表明，GBDT4CTRVis 在帮助理解模型机制和调整模型方面非常有用和有效。

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来源期刊

Journal of Visualization COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS-IMAGING SCIENCE & PHOTOGRAPHIC TECHNOLOGY

CiteScore

3.40

自引率

5.90%

发文量

审稿时长

>12 weeks

期刊介绍： Visualization is an interdisciplinary imaging science devoted to making the invisible visible through the techniques of experimental visualization and computer-aided visualization. The scope of the Journal is to provide a place to exchange information on the latest visualization technology and its application by the presentation of latest papers of both researchers and technicians.