Research Methodology for Analysis of E-Commerce User Activity Based on User Interest using Web Usage Mining


  • Saucha Diwandari Informatics Study Program, Faculty of Information Technology and Electrical, Universitas Teknologi Yogyakarta, Jl. Siliwangi, Yogyakarta 55285
  • Adhistya Erna Permanasari Department of Electrical Engineering and Information Technology, Faculty of Engineering, Universitas Gadjah Mada, Bulaksumur, Yogyakarta 55281,
  • Indriana Hidayah Department of Electrical Engineering and Information Technology, Faculty of Engineering, Universitas Gadjah Mada, Bulaksumur, Yogyakarta 55281,



classification, clickstream, data mining, e-commerce, user interest, web usage mining, web access log.


Visitor interaction with e-commerce websites generates large amounts of clickstream data stored in web access logs. From a business standpoint, clickstream data can be used as a means of finding information on user interest. In this paper, the authors propose a method to find user interest in products offered on e-commerce websites based on web usage mining of clickstream data. In this study, user interest was investigated using the PIE approach coupled with clustering and classification techniques. The experimental results showed that the method is able to assist in analyzing visitor behavior and user interest in e-commerce products by identifying those products that prompt visitor interest.


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Liu, B., Web Data Mining Exploring Hyperlinks, Contents, and Usage Data, Springer Berlin Heidelberg, 2011.

Liao, S-H., Chen, Y-J. & Lin, Y-T., Mining Customer Knowledge to Implement Online Shopping and Home Delivery for Hypermarkets, Expert Syst. Appl., 38(4), pp. 3982-3991, 2011.

Carmona C.J., Ramrez-Gallego, S., Torres, F., Bernal, E., Del Jesus, M. J. & Garca, S., Web Usage Mining to Improve the Design of an e-Commerce Website:, Expert Syst. Appl., 39(12), pp. 11243-11249, 2012.

Thorleuchter, D., Poel, D. van den, & Prinzie, A., Analyzing Existing Customers' Websites to Improve the Customer Acquisition Process as Well as the Profitability Prediction in B-to-B Marketing, Expert Syst. Appl., 39(3), pp. 2597-2605, 2012.

Thorleuchter, D. & Poel, D. van den, Using Webcrawling of Publicly Available Websites to Assess E-Commerce Relationships, presented at the Annual SRII Global Conference, SRII, pp. 402-410, 2012.

Srivastava, J., Cooley, R., Deshpande, M. & Tan, P., Web Usage Mining: Discovery and Applications of Usage Patterns from Web Data, 2000.

Su, Q. & Chen, L., A Method for Discovering Clusters of E-Commerce Interest Patterns Using Click-Stream Data, Electron. Commer. Res. Appl., 14(1), pp. 1-13, 2015.

Bucklin, R.E. & Sismeiro, C., Click Here for Internet Insight: Advances in Clickstream Data Analysis in Marketing, J. Interact. Mark., 3(1), pp. 35-48, 2009.

Chiang, R-D., Wang, Y-H. & Chu, H-C., Prediction of Members' Return Visit Rates Using a Time Factor, Electron. Commer. Res. Appl., 12(5), pp. 362-371, 2013.

Nottorf, F., Modeling the Clickstream Across Multiple Online Advertising Channels Using a Binary Logit With Bayesian Mixture of Normals, Electron. Commer. Res. Appl., 13(1), pp. 45-55, 2014.

Chen, C-H., Chiang, R-D., Wang, Y-H. & Chu, H-C., Combing Customer Profiles for Members' Return Visit Rate Predictions, Int. J. Innov. Comput. Inf. Control, 9(2), pp. 503-523, 2013.

Zhao, X., Niu, Z. & Chen, W., Interest before Liking: Two-step Recommendation Approaches, Knowl.-Based Syst., 48, pp. 46-56, 2013.

Cleger-Tamayo, S., Fernandez-Luna, J-M. & Huete, J-F., Top-N News Recommendations in Digital Newspapers, Knowl.-Based Syst., 27, pp. 180-189, 2012.

Park, Y-J. & Chang, K-N., Individual and Group Behavior-Based Customer Profile Model for Personalized Product Recommendation, Expert Syst. Appl., 36(2), Part 1, pp. 1932-1939, 2009.

Park, S., Suresh, N.C. & Jeong, B-K., Sequence-Based Clustering for Web Usage Mining: A New Experimental Framework and ANN-Enhanced K-Means Algorithm, Data Knowl. Eng., 65(3), pp. 512-543, 2008.

Zheng, L., Cui, S., Yue, D. & Zhao, X., User Interest Modeling Based on Browsing Behavior, presented at the ICACTE 2010-2010 3rd International Conference on Advanced Computer Theory and Engineering, Proceedings, 5, pp. V5455-V5458, 2010.

Kim, Y. S. & Yum, B-J., Recommender system Based on click Stream Data Using Association Rule Mining, Expert Syst. Appl., 38(10), pp. 13320-13327, 2011.

Pazzani, M. & Billsus, D., Learning and Revising User Profiles: The Identification of Interesting Web Sites, Mach. Learn., 27(3), pp. 313-331, 1997.

Chan, P.K., Constructing Web User Profiles: A Non-invasive Learning Approach, International Workshop on Web Usage Analysis and User Profiling, San Diego, CA, USA, pp. 39-55, 1999.

Li, Y., Feng, B-Q. & Wang, F., Page Interest Estimation Based on the User's Browsing Behavior, presented at the 2009 2nd International Conference on Information and Computing Science, ICIC 2009, 1, pp. 258-261, 2009.

Lee, C-H. & Fu, Y-H., Web Usage Mining Based on Clustering of Browsing Features, 8th International Conference on Intelligent System Design and Applications, 1, pp. 281-286, 2008.

White, R.W., Bailey, P. & Chen, L., Predicting User Interests from Contextual Information, presented at the Proceedings-32nd Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR, pp. 363-370, 2009.

Jalali, M., Mustapha, N., Sulaiman, M.N. & Mamat, A., WebPUM: A Web-Based Recommendation System to Predict User Future Movements, Expert Syst. Appl., 37(9), pp. 6201-6212, 2010.

Yu, J-X., Ou, Y., Zhang,C. & Zhang, S., Identifying Interesting Visitors Through Web Log Classification, IEEE Intell. Syst., 20(3), pp. 55-60, 2005.

Chitra, S. & Kalpana, B., Identifying Interesting Visitors Through Transductive Support Vector Machine Web Log Classifier, Int. Rev. Comput. Softw., 9(2), pp. 390-395, 2014.

Zeng, J., Zhang, S. & Wu, C., A Framework for WWW user Activity Analysis Based on User Interest, Knowl.-Based Syst., 21(8), pp. 905-910, 2008.

Claypool, M., Le, P., Wased, M. & Brown, D., Implicit Interest Indicators, presented at the International Conference on Intelligent User Interfaces, Proceedings IUI, pp. 33-40, 2001.

Y, Li. & Feng, B-Q., Page Interest Estimation Model Considering User Interest Drift, presented at the Proceedings of 4th International Conference on Computer Science and Education, ICCSE, pp. 1893-1896, 2009

Tavakolian, R. & Moghadam Charkari, N., A Novel Web Recommender System Considering Users' Need Evolution, 5th Int. Symp. Telecommun. IST 2010, pp. 738-743, 2010.

Su, F., Liu, Y., Zhou, L. & Ye, M., Information fusion of Crossing Network Communities Based on Analyzing Interests, presented at the Proceedings IEEE 5th International Conference on Bio-Inspired Computing: Theories and Applications, BIC-TA, pp. 835-839, 2010.

Bari, P. & Chaven, P., Page Interest Estimation using Apriori Algorithm, in International Journal of Advanced Research in Computer Engineering & Technology, 2(6), pp.1955-1959, 2013.

Diwandari, S., Permanasari, A. E., & Hidayah, I., Performance Analysis of Naive Bayes, PART and SMO for Classification of Page Interest in Web Usage Mining, in International Seminar on Intelligent Technology and Its Applications (ISITIA), pp. 39-44, 2015.

Perkowitz, M. & Etzioni, O., Adaptive Web sites: Automatically Synthesizing Web Pages, presented at the Proceedings of the National Conference on Artificial Intelligence, pp. 727-732. 1998.

Guerbas, A., Omar A., Omar Z & Mohamad N., Effective Web Log Mining and Online Navigational Pattern Prediction, Knowl.-Based Syst., 49, pp. 50-62, 2013.

Zhuang, W. & Zhang, Y., Identifying Erroneous Data Using Outlier Detection Techniques, in Ocean Biodiversity Informatics, Hamburg, Germany, 37, pp. 187-192, 2007

Cortes, C. & Vapnik, V., Support-vector Networks, Mach. Learn., 20(3), pp. 273-297, 1995.

Lin, C-J., Optimization, Support Vector Machines, and Machine Learning, 2005.

Suharjito, S., Diana, D. & Herianto, H., Implementation of Classification Technique in Web Usage Mining of Banking Company, in 2016 International Seminar on Intelligent Technology and Its Applications (ISITIA), pp. 211-218, 2016

Mehak, Kumar, M. & Aggarwal, N., Web Usage Mining: An Analysis, J. Emerg. Technol. Web Intell., 5(3), pp. 240-246, 2013.

Helmy, H., Online Shop Visitor Access Patterns Using a Weighted Graph Web Usage Mining (Case study"i: Koi Online shop), Thesis, Universitas Gadjah Mada, Yogyakarta, Indonesia, 2013.

Kim, H-R. & Chan, P. K., Implicit Indicators for Interesting Web Pages, presented at the WEBIST 2005-1st International Conference on Web Information Systems and Technologies, Proceedings, pp. 270-277, 2005.




How to Cite

Diwandari, S., Permanasari, A. E., & Hidayah, I. (2018). Research Methodology for Analysis of E-Commerce User Activity Based on User Interest using Web Usage Mining. Journal of ICT Research and Applications, 12(1), 54-69.




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