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Data Analysis and Knowledge Discovery  2018, Vol. 2 Issue (2): 74-85    DOI: 10.11925/infotech.2096-3467.2017.0886
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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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[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     
Received: 01 September 2017      Published: 07 March 2018
ZTFLH:  TP391.4 F713.8  

Cite this article:

Liu Hongwei,Gao Hongming,Chen Li,Zhan Mingjun,Liang Zhouyang. Identifying User Interests Based on Browsing Behaviors. Data Analysis and Knowledge Discovery, 2018, 2(2): 74-85.

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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
缩写 H A S D F G R B P Y V T C O
类名 主页 账户 付款
购物车 商品 评价 品牌或旗舰店 价格 人气 销量 商品
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
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
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
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
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**
[1] 戴德宝, 刘西洋, 范体军. “互联网+”时代网络个性化推荐采纳意愿影响因素研究[J]. 中国软科学, 2015(8): 163-172.
[1] (Dai Debao, Liu Xiyang, Fan Tijun.Research on the Adoption Intention of Online Personalized Recommender in the Internet Plus Era[J]. China Soft Science, 2015(8): 163-172.)
[2] Ding A W, Li S, Chatterjee P.Learning User Real-Time Intent for Optimal Dynamic Web Page Transformation[J]. Information Systems Research, 2015, 26(2): 339-359.
doi: 10.1287/isre.2015.0568
[3] Rana C, Jain S K.A Study of the Dynamic Features of Recommender Systems[J]. Artificial Intelligence Review, 2015, 43(1): 141-153.
doi: 10.1007/s10462-012-9359-6
[4] Mladenic D.Text-Learning and Related Intelligent Agents: A Survey[J]. IEEE Intelligent Systems & Their Applications, 2002, 14(4): 44-54.
doi: 10.1109/5254.784084
[5] Chu W, Park S T.Personalized Recommendation on Dynamic Content Using Predictive Bilinear Models[C]//Procedings of the 18th International Conference on World Wide Web, Madrid, Spain. New York, NY, USA: ACM, 2009: 691-700.
[6] Burke R.Hybrid Web Recommender Systems [A]// The Adaptive Web: Methods and Strategies of Web Personalization, Lecture Notes in Computer Science[M]. Springer, 2007: 377-408.
[7] Kardan A A, Ebrahimi M.A Novel Approach to Hybrid Recommendation Systems Based on Association Rules Mining for Content Recommendation in Asynchronous Discussion Groups[J]. Information Sciences, 2013, 219: 93-110.
doi: 10.1016/j.ins.2012.07.011
[8] Li C, Jiang Z.A Hybrid News Recommendation Algorithm Based on User’s Browsing Path[C]// Procedings of the IEEE/ACIS International Conference on Computer and Information Science, Okayama, Japan. IEEE, 2016: 1-4.
[9] 张奇. 天猫推荐算法实践 [EB/OL]. (2014-06-22). [2017- 07-10]. .
[9] (Zhang Qi. Recommendation of TMall [EB/OL]. (2014-06-22). [2017-07-10].
[10] Shang M S, Chen G X, Dai S X, et al.Interest-Driven Model for Human Dynamics[J]. Chinese Physics Letters, 2010, 27(4): 48701-48703.
doi: 10.1088/0256-307X/27/4/048701
[11] Barabási A L.The Origin of Bursts and Heavy Tails in Human Dynamics[J]. Nature, 2005, 435(7039): 207-211.
doi: 10.1038/nature03459 pmid: 15889093
[12] Zhao Z D, Yang Z, Zhang Z, et al. Emergence of Scaling in Human-Interest Dynamics [J]. Scientific Reports, 2013, 3(12). Article No.: 3472
doi: 10.1038/srep03472 pmid: 3858797
[13] Han X P, Zhou T, Wang B H.Modeling Human Dynamics with Adaptive Interest[J]. New Journal of Physics, 2007, 10(7): 1983-1989.
doi: 10.1088/1367-2630/10/7/073010
[14] Estrin D. Small Data, Where n = me[J]. Communications of the ACM, 2014, 57(4): 32-34.
[15] Zahoor S, Bedekar M, Kosamkar P K.User Implicit Interest Indicators Learned from the Browser on the Client Side[C]//Proceedings of International Conference on Information and Communication Technology for Competitive Strategies. ACM, 2014.
[16] Claypool M, Le P, Wased M, et al.Implicit Interest Indicators[C]//Proceedings of the 6th International Conference on Intelligent User Interfaces, New Mexico, USA. New York, NY, USA: ACM, 2000: 33-40.
[17] 崔春生. 基于隐式浏览输入的用户聚类分析[J]. 计算机应用研究, 2011, 28(8): 2862-2864.
doi: 10.3969/j.issn.1001-3695.2011.08.017
[17] (Cui Chunsheng.User Clustering Analysis Based on Implicit Navigation[J]. Application Research of Computers, 2011, 28(8): 2862-2864.)
doi: 10.3969/j.issn.1001-3695.2011.08.017
[18] Lieberman H.Letizia: An Agent That Assists Web Browsing[C]//Proceedings of the 14th International Joint Conference on Artificial Intelligence.1995, 1: 924-929.
[19] Kuo R J, Liao J L, Tu C.Integration of ART2 Neural Network and Genetic K-means Algorithm for Analyzing Web Browsing Paths in Electronic Commerce[J]. Decision Support Systems, 2005, 40(2): 355-374.
doi: 10.1016/j.dss.2004.04.010
[20] Jayawardhena C, Dennis C, Wright L T.Consumers Online: Intentions, Orientations and Segmentation[J]. International Journal of Retail & Distribution Management, 2007, 35(6): 515-526.
doi: 10.1108/09590550710750377
[21] 朱志国. 基于隐马尔可夫链模型的电子商务用户兴趣导航模式发现[J]. 中国管理科学, 2014, 22(4): 67-73.
[21] (Zhu Zhiguo.Discovery of E-Commerce Users’ Interest Navigation Patterns Based on Hidden Markov Chains Model[J].Chinese Journal of Management Science, 2014, 22(4): 67-73.)
[22] 付关友, 朱征宇. 个性化服务中基于行为分析的用户兴趣建模[J]. 计算机工程与科学, 2005, 27(12): 76-78.
doi: 10.3969/j.issn.1007-130X.2005.12.026
[22] (Fu Guanyou, Zhu Zhengyu.A User Interest Modele Based on the Analysis of User Behaviors for Personalization[J]. Computer Engineering & Science, 2005, 27(12): 76-78.)
doi: 10.3969/j.issn.1007-130X.2005.12.026
[23] Li B, Sun B, Montgomery A L.Cross-Selling the Right Product to the Right Customer at the Right Time[J]. Journal of Marketing Research, 2011, 48(4): 683-700.
doi: 10.2307/23033447
[24] Machleit K A, Allen C T, Madden T J.The Mature Brand and Brand Interest: An Alternative Consequence of Ad-Evoked Affect[J]. Journal of Marketing, 1993, 57(4): 72-82.
doi: 10.2307/1252220
[25] Duan W J, Gu B, Whinston A B.The Dynamics of Online Word-Of-Mouth and Product Sales — An Empirical Investigation of the Movie Industry[J]. Journal of Retailing, 2008, 84(2): 233-242.
doi: 10.1016/j.jretai.2008.04.005
[26] Moe W W.Buying, Searching, or Browsing: Differentiating Between Online Shoppers Using In-Store Navigational Clickstream[J]. Journal of Consumer Psychology, 2003, 13(1): 29-39.
doi: 10.1207/S15327663JCP13-1&2_03
[27] Montgomery A L, Li S, Srinivasan K, et al.Modeling Online Browsing and Path Analysis Using Clickstream Data[J]. Marketing Science, 2004, 23(4): 579-595.
doi: 10.1287/mksc.1040.0073
[28] Howard J A, Sheth J N.The Theory of Buyer Behavior[M]. New York: Wiley, 1969: 421-449.
[29] MacQueen J. Some Methods for Classification and Analysis of MultiVariate Observations[C]// Proceedings of the 5th Berkeley Symposium on Mathematical Statistics and Probability. 1967: 281-297.
[30] Ball G H, Hall D J.A Clustering Technique for Summarizing Multivariate Data[J]. Systems Research & Behavioral Science, 1967, 12(2): 153-155.
doi: 10.1002/bs.3830120210 pmid: 6030099
[31] Jain A K.Data Clustering: 50 Years Beyond K-means[J]. Pattern Recognition Letters, 2010, 31(8): 651-666.
doi: 10.1016/j.patrec.2009.09.011
[32] Steinley D.K-Means Clustering: A Half-Century Synthesis[J]. British Journal of Mathematical and Statistical Psychology, 2006, 59(1): 1-34.
doi: 10.1348/000711005X48266 pmid: 16709277
[33] Savaresi S M, Boley D L.On the Performance of Bisecting K-Means and PDDP[J]. Intelligent Data Analysis, 2004, 8(4): 345-362.
doi: 10.1137/1.9781611972719.5
[34] Rousseeuw P.Silhouettes: A Graphical Aid to the Interpretation and Validation of Cluster Analysis[J]. Journal of Computational & Applied Mathematics, 1987, 20: 53-65.
doi: 10.1016/0377-0427(87)90125-7
[35] Kodinariya T M, Makwana P R.Review on Determining Number of Cluster in K-means Clustering[J]. International Journal of Advance Research in Computer Science and Management Studies, 2013, 1(6): 90-95.
[36] Samadi M, Yaghoob-Nejadi A.A Survey of the Effect of Consumers’ Perceived Risk on Purchase Intention in e-Shopping[J]. Business Intelligence Journal, 2009, 2(2): 261-275.
doi: 10.4236/jssm.2015.81012
[37] Close A G, Kukar-Kinney M.Beyond Buying: Motivations Behind Consumers’ Online Shopping Cart Use[J]. Journal of Business Research, 2010, 63(9): 986-992.
doi: 10.1016/j.jbusres.2009.01.022
[38] 殷晨. 网络促销对消费者冲动性购买行为的影响研究 [D]. 济南: 山东大学, 2013.
[38] (Yin Chen.Research on the Influence of the Network Promtion on Consumers’ Impluse Buying Behavior[D]. Ji’nan: Shandong University, 2013.)
[39] Asiegbu I F, Powei D M, Iruka C H.Consumer Attitude: Some Reflections on Its Concept, Trilogy, Relationship with Consumer Behavior, and Marketing Implications[J]. European Journal of Business and Management, 2012, 4(13): 38-50.
[40] Hawkins D I, Mothersbaugh D L, Best R J.Consumer Behavior: Building Marketing Strategy[M]. McGraw-Hill Irwin, 2013.
[41] Fishbein M, Ajzen I. Belief, Attitude, Intention, and Behavior: An Introduction to Theory and Research[M]. Addison-Wesley Pub. Co., 1975.
[42] Bundesen C, Habekost T, Kyllingsbaek S.A Neural Theory of Visual Attention: Bridging Cognition and Neurophysiology[J]. Psychological Review, 2005, 112(2): 291-328.
doi: 10.1037/0033-295X.112.2.291
[43] Russo J E, Leclerc F.An Eye-Fixation Analysis of Choice Processes for Consumer Nondurables[J]. Journal of Consumer Research, 1994, 21(2): 274-290.
doi: 10.1086/209397
[44] Clement J.Visual Influence on In-store Buying Decisions: An Eye-Track Experiment on the Visual Influence of Packaging Design[J]. Journal of Marketing Management, 2007, 23(9-10): 917-928.
doi: 10.1362/026725707X250395
[45] Glaholt M G, Reingold E M.Eye Movement Monitoring as a Process Tracing Methodology in Decision Making Research[J]. Journal of Neuroscience, Psychology, and Economics, 2011, 4(2): 125-146.
doi: 10.1037/a0020692
[46] Glöckner A, Herbold A K.An Eye‐Tracking Study on Information Processing in Risky Decisions: Evidence for Compensatory Strategies Based on Automatic Processes[J]. Journal of Behavioral Decision Making, 2011, 24(1): 71-98.
doi: 10.1002/bdm.684
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