Purchase Prediction via Machine Learning in Mobile Commerce

机译：通过移动商务中的机器学习进行购买预测

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摘要

In this paper, we propose a machine learning approach to solve the purchase prediction task launched by the Alibaba Group. In detail, we treat this task as a binary classification problem and explore five kinds of features to learn potential model of the influence of historical behaviors. These features include user quality, item quality, category quality, user-item interaction and user-category interaction. Due to the nature of mobile platform, time factor and spacial factor are considered specially. Our approach ranks the 26th place among 7186 teams in this task.

机译：在本文中，我们提出了一种机器学习方法来解决阿里巴巴集团发起的购买预测任务。详细地，我们将此任务视为二元分类问题，并探索五种特征以学习历史行为影响的潜在模型。这些功能包括用户质量，项目质量，类别质量，用户项目交互和用户类别交互。由于移动平台的性质，需要特别考虑时间因素和空间因素。在此任务中，我们的方法在7186个团队中排名26。

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来源
《Natural language understanding and intelligent applications》|2016年|506-513|共8页
会议地点 Kunming(CN)
作者
Chao Lv; Yansong Feng; Dongyan Zhao;
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作者单位

Institute of Computer Science and Technology, Peking University, Beijing 100871, China;

Institute of Computer Science and Technology, Peking University, Beijing 100871, China;

Institute of Computer Science and Technology, Peking University, Beijing 100871, China;

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原文格式 PDF
正文语种 eng
中图分类
关键词
Purchase prediction; Machine learning;

机译：购买预测；机器学习;

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3. Mobile Commerce Competitive Advantage: A Quantitative Study of Variables that Predict M-Commerce Purchase Intentions [J] . Robert Blaise, Michael Halloran, Marc Muchnick Journal of Internet Commerce . 2018,第1a4期

机译：移动商务竞争优势：预测移动商务购买意愿的变量的定量研究
4. Purchase Prediction via Machine Learning in Mobile Commerce [C] . Chao Lv, Yansong Feng, Dongyan Zhao International conference on computer processing of oriental languages . 2016

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Purchase Prediction via Machine Learning in Mobile Commerce

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