Mining Users’ Preference Similarities in E-commerce Systems Based on Webpage Navigation Logs

Ping Li, Chunxue Wu, Shaozhong Zhang, Xinwu Yu, Haidong Zhong


Mining users’ preference patterns in e-commerce systems is a fertile area for a great many application directions, such as shopping intention analysis, prediction and personalized recommendation. The web page navigation logs contain much potentially useful information, and provide opportunities for understanding the correlation between users’ browsing patterns and what they want to buy. In this article, we propose a web browsing history mining based user preference discovery method for e-commerce systems. First of all, a user-browsing-history-hierarchical-presentationgraph to established to model the web browsing histories of an individual in common e-commerce systems, and secondly an interested web page detection algorithm is designed to extract users’ preference. Finally, a new method called UPSAWBH (User Preference Similarity Calculation Algorithm Based on Web Browsing History), which measure the level of users’ preference similarity on the basis of their web page click patterns, is put forward. In the proposed UPSAWBH, we take two factors into account: 1) the number of shared web page click sequence, and 2) the property of the clicked web page that reflects users’ shopping preference in e-commerce systems. We conduct experiments on real dataset, which is extracted from the server of our self-developed e-commerce system. The results indicate a good effectiveness of the proposed approach.


web browsing history mining, e-commerce, preference, recommendation. Copyright ©

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Abraham S., Lai P.S. (2011); Spatio-temporal Similarity ofWeb User Session Trajectories and Applications in Dark Web Research, Proceedings of the Pacific Asia Workshop on Intelligence and Security Informatics, Beijing, China, 2011. doi:10.1007/978-3-642-22039-5_1.

Adomavicius G., Tuzhilin A. (2005); Towards the next generation of recommender systems: A survey of the state-of-the-art and possible extensions, IEEE Transaction on Knowledge and Data Engineering (TKDE), 17(6), 734-749, 2005. doi:10.1109/TKDE.2005.99.

Becchetti L. et al. (2014); A lightweight privacy preserving SMS - based recommendation system for mobile users, Knowledge and Information Systems, 40(1), 49-77, 2014.

Borges J., Levene M. (2000); Web usage analysis and user profiling, Chapter: Data mining of user navigation patterns (92-112), San Diego, CA, USA; Springer Berlin Heidelberg, 2000.

Chen D.-N. et al. (2010); AWeb-based personalized recommendation system for mobile phone selection: Design, implementation, and evaluation, Expert Systems with Applications, 37(12), 8201-8210, 2010. doi:10.1016/j.eswa.2010.05.066.

Cheng A.-J. et al. (2011); Personalized travel recommendation by mining people attributes from community-contributed photos, Proceedings of the 19th ACM international conference on Multimedia, Scottsdale, Arizona, USA; ACM, 83-92, 2011.

Dao T.H., Jeong S.R., Ahn H. (2012); A novel recommendation model of location-based advertising: Context-Aware Collaborative Filtering using GA approach, Expert Systems with Applications, 39(3), 3731-3739, 2012. doi:10.1016/j.eswa.2011.09.070.

Eckhardt A. (2012); Similarity of users' (content-based) preference models for Collaborative filtering in few ratings scenario, Expert Systems with Applications, 39(14), 11511-11516, 2012. doi:10.1016/j.eswa.2012.01.177.

Eirinaki B.M., M Vazirgiannis M. (2003); Web mining for web personalization, ACM Transactions on Internet Technology, 3(1), 1-27, 2003.

Ezeife C.I., Lu Y. (2005); Mining web log sequential patterns with position coded pre-order linked WAP-tree, Data Mining and Knowledge Discovery, 10(1), 5-38, 2005. doi:10.1007/s10618-005-0248-3.

He S., Fang M. (2008); Personalized recommendation based on ontology inference in Ecommerce, Proceedings of the International Conference on Management of e-Commerce and e-Government, 192-195, 2008. doi:10.1109/icmecg.2008.24.

Gan M., Jiang R. (2013); Constructing a user similarity network to remove adverse influence of popular objects for personalized recommendation, Expert Systems with Applications, 40(10), 4044-4053, 2013. doi:10.1016/j.eswa.2013.01.004.

Guy I. et al. (2010); Same places, same things, same people?: mining user similarity on social media, Proceedings of the 2010 ACM conference on Computer supported cooperative work, Savannah, Georgia, USA, 41-50, 2010. doi:10.1145/1718918.1718928.

Leavitt N. (2006); Recommendation technology: Will it boost E-Commerce, Computer, 39(5), 13-16, 2006. doi:10.1109/MC.2006.176.

Lee M.-J., Chung C.-W. (2011); A user similarity calculation based on the location for social network services, Proceedings of the Database Systems for Advanced Applications, Hong Kong, China, Springer, 38-52, 2011. doi:10.1007/978-3-642-20149-3_5.

Li P. et al. (2007); Preference update for e-commerce applications: Model, language, and processing, Electronic Commerce Research, 7(1), 17-44, 2007. doi:10.1007/s10660-006-0061-0.

Li Q. et al. (2008); Mining user similarity based on location history, Proceedings of the ACM SIGSPATIAL GIS, Irvine, CA, USA, 2008. doi:10.1145/1463434.1463477.

Linden G., Smith B., York J.(2003); recommendations: item-to-item collaborative filtering, IEEE Internet Computing, 7(1), 76-80, 2003. doi:10.1109/MIC.2003.1167344.

Papazoglou M.P. (2001); Agent-oriented technology in support of e-business - Enabling the development of "intelligent" business agents for adaptive, reusable software, Communications of the ACM, 44(4), 71-77, 2001. doi:10.1145/367211.367268.

Park S.T., Pennock D., Madani O. (2006); Collaborative filtering for robust cold-start recommendations, Proceedings of the 12th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, New York, USA, 2006.

Sarwar B.M. (2001); Sparsity, scalability and distribution in recommender systems, Minneapolis, USA, University of Minnesota, 2001.

Shahabi C. et al. (1997); Knowledge discovery from users web-page navigation, Proceedings of the Seventh International Workshop on Research Issues in Data Engineering, Birmingham, England, 1997. doi:10.1109/RIDE.1997.583692.

Song Q., Shepperd M. (2005); Mining web browsing patterns for E-commerce, Computers in Industry, 57(7), 622-630, 2006. doi:10.1016/j.compind.2005.11.006.

Srivastava J. et al. (2000); Web usage mining: Discovery and applications of usage patterns from web data, ACM SIGKDD Explorations Newsletter, 1(2), 12-23, 2000.

Turban E. et al. (2009); Electronic Commerce, NJ, USA: Prentice Hall Press, 2009.

Waga K., Tabarcea A., Franti P. (2011); Context aware recommendation of location-based data, Proceedings of the 15th International Conference on System Theory, Control, and Computing (ICSTCC), Sinaia, Romania, 1-6, 2011.

Wang Y.-T., Lee A.J.T. (2011); Mining web navigation patterns with a path traversal graph, Expert Systems with Applications, 38(6), 7112-7122, 2011. doi:10.1016/j.eswa.2010.12.058.

Woerndl W., Brocco M., Eigner R. (2009); Context-aware recommender systems in mobile scenarios, International Journal of Information Technology and Web Engineering, 4(1), 67-85, 2009. doi:10.4018/jitwe.2009010105.

Wu C., Hou F. (2011); Design and Optimization of Redundant ControlNet Networking Control System, Process Automation Instrumentation, 32(3), 50-56, 2011.

Wu C., Yu Z. (2005); Data transmission with data package dropout and control method on NCS, Control & Automation, 10, 39-41, 2005.

Ying J. J.-C. et al. (2010); Mining user similarity from semantic trajectories, Proceedings of the 2nd ACM SIGSPATIAL International Workshop on Location Based Social Networks, San Jose, California, 19-26, 2010. doi:10.1145/1867699.1867703.

YU X.B., GUO S.S., HUANG X.R. (2010); Intelligent e-commerce based on Web usage mining and its application (in Chinese), Computer Integrated Manufacturing Systems, 16(2), 439-448, 2010.

Zhong H. et al. (2014); Study on Directed Trust Graph Based Recommendation for Ecommerce System, International Journal of Computers Communications & Control, 9(4): 510-523, 2014.


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