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| Identifying User Interests Based on Browsing Behaviors |
Liu Hongwei1, Gao Hongming1, Chen Li2( ), Zhan Mingjun1, Liang Zhouyang1 |
1(School of Management, Guangdong University of Technology, Guangzhou 510520, China)
2(Guangdong Youth Vocational College, Guangzhou 510507, China) |
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Abstract [Objective] This paper proposes a model to identify the interests of online shoppers based on their browsing behaviors, aiming to improve the personalized recommendation services. [Methods] First, we launched experiment to collect clickstream data from Taobao and TMall. Second, we used the Bisecting K-means algorithm to analyze the retrieved data. Finally, we established the relationship mapping structure between interests and behaviors. [Results] We found four types of user’s implicit interests: Attention, Comprehension, Attitudes and Intention. Users with the Attitude and Intention types tended to make purchase. The characteristics of browsing paths were different among the users. [Limitations] We did not examine unstructured data, i.e., online sales advertisements, in this study. [Conclusions] This paper investigates the user interests in online shopping, and then improve the personalized recommendation services of the E-commerce platforms.
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Received: 01 September 2017
Published: 07 March 2018
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