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数据分析与知识发现  2018, Vol. 2 Issue (2): 74-85    DOI: 10.11925/infotech.2096-3467.2017.0886
  研究论文 本期目录 | 过刊浏览 | 高级检索 |
基于用户浏览行为的兴趣识别管理模型*
刘洪伟1,高鸿铭1,陈丽2(),詹明君1,梁周扬1
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)
Identifying User Interests Based on Browsing Behaviors
Hongwei Liu1,Hongming Gao1,Li Chen2(),Mingjun Zhan1,Zhouyang Liang1
1(School of Management, Guangdong University of Technology, Guangzhou 510520, China)
2(Guangdong Youth Vocational College, Guangzhou 510507, China)
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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。

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刘洪伟
高鸿铭
陈丽
詹明君
梁周扬
关键词 隐式兴趣点击流Bisecting K-means算法    
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.

Key wordsImplicit Interest    Clickstream    Bisecting K-means Algorithm
收稿日期: 2017-09-01     
基金资助:*本文系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)和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
引用本文:   
刘洪伟,高鸿铭,陈丽,詹明君,梁周扬. 基于用户浏览行为的兴趣识别管理模型*[J]. 数据分析与知识发现, 2018, 2(2): 74-85.
Hongwei Liu,Hongming Gao,Li Chen,Mingjun Zhan,Zhouyang Liang. Identifying User Interests Based on Browsing Behaviors. Data Analysis and Knowledge Discovery, DOI:10.11925/infotech.2096-3467.2017.0886.
链接本文:  
http://manu44.magtech.com.cn/Jwk_infotech_wk3/CN/10.11925/infotech.2096-3467.2017.0886
图1  本文研究框架
字段 含义
user_Id 用户ID
sessionId 会话ID
tabId 标签页记录ID
title 网页主题
url 用户访问地址
visitedTime 用户访问时间
goodlist 商品列表
Info 鼠标点击信息
表1  浏览器日志字段及含义
图2  三阶段实验设计框架
缩写 H A S D F G R B P Y V T C O
类名 主页 账户 付款
购买
加入购物车&
收藏夹
购物车 商品 评价 品牌或旗舰店 价格 人气 销量 商品
属性
目录 其他
频数 138 96 7 30 52 170 11 142 17 5 4 588 438 74
频率(%) 7.79 5.42 0.40 1.69 2.93 9.59 0.62 8.01 0.96 0.28 0.23 33.18 24.72 4.18
表2  页面类别统计描述表
变量 均值 标准差 最小值 中位数 最大值
页面持续时间(秒) 12.28 45.32 0.00 3.00 1492.00
页面相对浏览
时间率(%)
0.71 3.42 0.00 0.09 100.00
页面点击率(%) 27.67 18.76 0.27 26.01 100.00
会话访问深度(页) 28.20 25.72 2.00 22.00 102.00
表3  兴趣指标描述性统计
图3  轮廓系数与K的关系
动态兴趣 Time Timeratio Clickratio Sessiondepth
第1簇 5.283270 0.5805210 50.57808 16.92205
第2簇 7.042510 0.2328121 19.05581 64.23077
第3簇 11.558824 0.6666170 17.15118 12.02801
第4簇 155.5405 8.1338870 21.005 19.62162
表4  4类用户兴趣的簇中心
图4  不同兴趣状态的数据分布可视化
4类动态兴趣 相关系数
第1簇 /
第2簇 -0.022
第3簇 -0.081
第4簇 0.679**
表5  各簇中使用购物车行为次数与实际购买次数相关检验
图5  不同兴趣状态的D类页面频数均值估计碎石图
图6  某会话中的用户部分兴趣状态-浏览路径图
图7  不同状态下的一步转移概率可视化表示
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