基于用户浏览行为的兴趣识别管理模型  

Identifying User Interests Based on Browsing Behaviors

作　　者：刘洪伟[1] 高鸿铭 陈丽[2] 詹明君[1] 梁周扬[1] Liu Hongwei1, Gao Hongming1, Chen Li2, Zhan Mingjun1 ,Liang Zhouyang1(1School of Management, Guangdong University of Technology, Guangzhou 510520, China;2Guangdong Youth Vocational College, Guangzhou 510507, Chin)

机构地区：[1]广东工业大学管理学院,广州510520 [2]广东青年职业学院,广州510507

出　　处：《数据分析与知识发现》2018年第2期74-85,共12页Data Analysis and Knowledge Discovery

基　　金：国家自然科学基金资助项目"电子商务交互式决策助手对用户购物决策行为的影响与演化研究"(项目编号:71671048);广东工业大学研究生创新项目"基于双元创新活动的顾客参与对口碑推荐的影响研究"(项目编号:2017YJSCX034)的研究成果之一

摘　　要：【目的】了解用户在线购物中的兴趣需求变化有利于个性化推荐。本文提出结合用户浏览行为分析的隐式动态兴趣识别和管理模型。【方法】通过三阶段实验构造用户点击流数据,以天猫和淘宝网页功能键为数据粒度对页面分类,再采用Bisecting K-means聚类算法进行兴趣状态挖掘,最后总结归纳兴趣与行为的特征映射。【结果】用户隐式兴趣存在4种状态:关注、理解信息、态度和购买意图,在态度和购买意图状态下,更倾向于产生购买;在不同状态的浏览路径特征有所差异。【局限】未添加网页广告促销等非结构化数据进行分析。【结论】从实时动态兴趣的角度,对购物决策中兴趣的状态进行验证挖掘,拓展动态兴趣研究;为电商网站管理用户行为提供了一个实现动态个性化推荐的视角。[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.

关 键 词：隐式兴趣 点击流 Bisecting K-MEANS算法 

分 类 号：TP391.4[自动化与计算机技术—计算机应用技术] F713.8[自动化与计算机技术—计算机科学与技术]