Machine Learning Approach to Identify Users Across Their Digital Devices

Thakur Raj Anand, Oleksii Renov
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引用次数: 9

Abstract

This paper discusses methods to identify individual users across their digital devices as part of the ICDM 2015 competition hosted on Kaggle. The competition's data set and prize pool were provided by http://www.drawbrid.ge/ in sponsorship with the ICDM 2015 conference. The methods described in this paper focuses on feature engineering and generic machine learning algorithms like Extreme Gradient Boosting (xgboost), Follow the Reguralized Leader Proximal etc. Machine learning algorithms discussed in this paper can help improve the marketer's ability to identify individual users as they switch between devices and show relevant content/recommendation to users wherever they go.
跨数字设备识别用户的机器学习方法
本文讨论了在数字设备上识别个人用户的方法,作为Kaggle主办的ICDM 2015竞赛的一部分。比赛的数据集和奖金池由http://www.drawbrid.ge/与ICDM 2015会议赞助提供。本文描述的方法侧重于特征工程和通用机器学习算法,如极端梯度增强(xgboost),遵循正则化Leader Proximal等。本文中讨论的机器学习算法可以帮助营销人员提高识别个人用户的能力,因为他们在设备之间切换,并向用户显示相关的内容/推荐。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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