%A Liu Hongwei,Gao Hongming,Chen Li,Zhan Mingjun,Liang Zhouyang %T Identifying User Interests Based on Browsing Behaviors %0 Journal Article %D 2018 %J Data Analysis and Knowledge Discovery %R 10.11925/infotech.2096-3467.2017.0886 %P 74-85 %V 2 %N 2 %U {https://manu44.magtech.com.cn/Jwk_infotech_wk3/CN/abstract/article_4477.shtml} %8 2018-02-25 %X

[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.