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Purchase Prediction via Machine Learning in Mobile Commerce

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Natural Language Understanding and Intelligent Applications (ICCPOL 2016, NLPCC 2016)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 10102))

Abstract

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.

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Notes

  1. 1.

    http://tianchi.aliyun.com.

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Acknowledgement

The work reported in this paper was supported by the National Natural Science Foundation of China Grant 61272344 and 61370116.

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Authors and Affiliations

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

    Chao Lv, Yansong Feng & Dongyan Zhao

Authors
  1. Chao Lv
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  2. Yansong Feng
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  3. Dongyan Zhao
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Corresponding author

Correspondence to Yansong Feng .

Editor information

Editors and Affiliations

  1. Microsoft Research Asia, Beijing, China

    Chin-Yew Lin

  2. Brandeis University, Waltham, Massachusetts, USA

    Nianwen Xue

  3. Peking University, Beijing, China

    Dongyan Zhao

  4. Fudan University, Shanghai, China

    Xuanjing Huang

  5. Peking University, Beijing, China

    Yansong Feng

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© 2016 Springer International Publishing AG

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Cite this paper

Lv, C., Feng, Y., Zhao, D. (2016). Purchase Prediction via Machine Learning in Mobile Commerce. In: Lin, CY., Xue, N., Zhao, D., Huang, X., Feng, Y. (eds) Natural Language Understanding and Intelligent Applications. ICCPOL NLPCC 2016 2016. Lecture Notes in Computer Science(), vol 10102. Springer, Cham. https://doi.org/10.1007/978-3-319-50496-4_43

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  • DOI: https://doi.org/10.1007/978-3-319-50496-4_43

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-50495-7

  • Online ISBN: 978-3-319-50496-4

  • eBook Packages: Computer ScienceComputer Science (R0)

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