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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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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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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