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Profiling users by online shopping behaviors

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Online shopping has been prevalent in our daily life. Profiling users and understanding their browsing behaviors are critical for enhancing shopping experience and maximizing sales revenue. In this paper, based on a one-month dataset recording 2 million users’ 67 million online shopping and browsing logs, we seek to understand how users browse and shop products, and how distinct these behaviors are. We find that there exist dedicate groups of users that prefer certain product categories corresponding to similar demands. Moreover, distinct differences of behaviors exist in categories, where repetitive and targeted browsing are two major prevalent patterns.

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    These include Phone & Accessories (MP & AC), PC & Office (PC & OF), Books & CDs (BK & CD), Clothes (CL), House Decorations (DE), Household Appliances (HA), Sports & Health (SP & HE), Gifts & Bags (GI & BA), Cosmetics (CM), Maternity & Child (MA & CH) and Digital Products (DP).


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Correspondence to Yong Li.

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Yan, H., Wang, Z., Lin, T. et al. Profiling users by online shopping behaviors. Multimed Tools Appl 77, 21935–21945 (2018). https://doi.org/10.1007/s11042-017-5365-7

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  • Social network
  • User behavior analytics
  • Data analysis