Application of the Information Bottleneck method to discover user profiles in a Web store

References

• Abbot, D. 2014. Applied predictive analytics. Principles and techniques for the professional data analyst. Indianapolis, IN: Wiley.
   Google Scholar
• Adnan, M., M. Nagi, K. Kianmehr, R. Tahboub, M. Ridley, and J. Rokne. 2011. Promoting where, when and what? An analysis of Web logs by integrating data mining and social network techniques to guide ecommerce business promotions. Social Network Analysis and Mining 1 (3):173–85. doi:10.1007/s13278-016-0375-4.
   Google Scholar
• Adomavicius, G., and A. Tuzhilin. 2005. Toward the next generation of recommender systems: A survey of the state-of-the-art and possible extensions. IEEE Transactions on Knowledge and Data Engineering 17 (6):734–49. doi:10.1109/TKDE.2005.99.
   Web of Science ®Google Scholar
• Carmona, C. J., S. Ramírez-Gallego, F. Torres, E. Bernal, M. J. Del Jesus, and S. García. 2012. Web usage mining to improve the design of an e-commerce website: OrOliveSur.com. Expert Systems with Applications 39 (12):11243–49. doi:10.1016/j.eswa.2012.03.046.
   Web of Science ®Google Scholar
• Chang, H.-J., L.-P. Hung, and C.-L. Ho. 2007. An anticipation model of potential customers’ purchasing behavior based on clustering analysis and association rules analysis. Expert Systems with Applications 32 (3):753–64. doi:10.1016/j.eswa.2006.01.049.
   Web of Science ®Google Scholar
• Chen, Z., A. W.-C. Fu, and F. C.-H. Tong. 2003. Optimal algorithms for finding user access sessions from very large Web logs. World Wide Web 6:259–79. doi:10.1023/A:1024606901978.
   Google Scholar
• Chen, Z.-Y., Z.-P. Fan, and M. Sun. 2012. A hierarchical multiple kernel support vector machine for customer churn prediction using longitudinal behavioral data. European Journal of Operational Research 223 (2):461–72. doi:10.1016/j.ejor.2012.06.040.
   Web of Science ®Google Scholar
• Cheng, C.-H., and Y.-S. Chen. 2009. Classifying the segmentation of customer value via RFM model and RS theory. Expert Systems with Applications 36 (3),Part I):4176–84. doi:10.1016/j.eswa.2008.04.003.
   Web of Science ®Google Scholar
• Cho, Y. H., and J. K. Kim. 2004. Application of Web usage mining and product taxonomy to collaborative recommendations in e-commerce. Expert Systems with Applications 26 (2):233–46. doi:10.1016/S0957-4174(03)00138-6.
   Web of Science ®Google Scholar
• Chou, P.-H., P.-H. Li, -K.-K. Chen, and M.-J. Wu. 2010. Integrating web mining and neural network for personalized e-commerce automatic service. Expert Systems with Applications 37 (4):2898–910. doi:10.1016/j.eswa.2009.09.047.
   Web of Science ®Google Scholar
• Du, W., and S. Tan. 2009. An iterative reinforcement approach for fine-grained opinion mining. In Proceedings of Human Language Technologies: The 2009 Annual Conference of the North American Chapter of the Association for Computational Linguistics (NAACL’09), ed. U. Germann, C. Shah, S. Stoyanchev, C. P. Rosé, and A. Sarkar, 486–93. Boulder, CO: Association for Computational Linguistics.
   Google Scholar
• Ganu, G., Y. Kakodkar, and A. Marian. 2013. Improving the quality of predictions using textual information in online user reviews. Information Systems 38 (1):1–15. doi:10.1016/j.is.2012.03.001.
   Web of Science ®Google Scholar
• Gordon, S., H. Greenspan, and J. Goldberger. 2003. Applying the Information Bottleneck principle to unsupervised clustering of discrete and continuous image representations. In Proceedings of the 9th IEEE International Conference on Computer Vision (ICCV’03), 370–77. New York, NY: IEEE.
   Google Scholar
• Hecht, R. M., and N. Tishby. 2005. Extraction of relevant speech features using the Information Bottleneck method. Paper presented at the 9th European Conference on Speech Communication and Technology (INTERSPEECH’05 - Eurospeech), Lisbon, Portugal, September 4–8.
   Google Scholar
• Hop, W. 2013. Web-shop order prediction using machine learning. MsC. thesis, Erasmus University Rotterdam.
   Google Scholar
• Huiying, Z., and L. Wei. 2004. An intelligent algorithm of data pre-processing in Web usage mining. In Proceedings of the 5th World Congress on Intelligent Control and Automation (WCICA’04), ed. Z. Da Xue, vol. 4, 3119–23. New York, NY: IEEE. doi:10.1109/WCICA.2004.1343095.
   Google Scholar
• Ikeda, M., M. Yoshida, S. Ono, H. Nakagawa, and I. Sato. 2009. Person name disambiguation on the Web by two-stage clustering. Paper presented at the 18th International Conference on World Wide Web (WWW’09), Madrid, Spain, April 20–24.
   Google Scholar
• Jenamani, M., P. K. J. Mohapatra, and S. Ghose. 2003. A stochastic model of e-customer behavior. Electronic Commerce Research and Applications 2 (1):81–94. doi:10.1016/S1567-4223(03)00010-3.
   Google Scholar
• Jiang, X.-M., W.-G. Song, and W.-G. Feng. 2006. Optimizing collaborative filtering by interpolating the individual and group behaviors. In Proceedings of the 8th Asia-Pacific Web Conference (APWeb’06), LNCS 3841, ed. X. Zhou, J. Li, H. T. Shen, M. Kitsuregawa, and Y. Zhang, 568–78. Berlin Heidelberg, Germany: Springer-Verlag.
   Google Scholar
• Joshi, A., K. Joshi, and R. Krishnapuram. 2000. On Mining Web access logs. Paper presented at the ACM SIGMOD Workshop on Research Issues in Data Mining and Knowledge Discovery (DMKD’00), Dallas, TX, May 14.
   Google Scholar
• Kumar, R., and P. K. Bala. 2017. Recommendation engine based on derived wisdom for more similar item neighbors. Information Systems and e-Business Management 15 (3):661–87. doi:10.1007/s10257-016-0322-y.
   Web of Science ®Google Scholar
• Kwan, I. S. Y., J. Fong, and H. K. Wong. 2005. An e-customer behavior model with online analytical mining for internet marketing planning. Decision Support Systems 41 (1):189–204. doi:10.1016/j.dss.2004.11.012.
   Web of Science ®Google Scholar
• Ling, X., G.-R. Xue, W. Dai, Y. Jiang, Q. Yang, and Y. Yu. 2008. Can chinese Web pages be classified with English data source? Paper presented at the 17th International Conference on World Wide Web (WWW’08), Beijing, China April 21–25.
   Google Scholar
• Menascé, D. A., V. A. F. Almeida, R. Fonseca, and M. A. Mendes. 1999. A methodology for workload characterization of e-commerce sites. Paper presented at the 1st ACM conference on Electronic Commerce, Denver, CO, November 3–5. doi: 10.1145/336992.337024.
   Google Scholar
• Mohammadnezhad, M., and M. Mahdavi. 2012. Providing a model for predicting tour sale in mobile e-tourism recommender systems. International Journal of Information Technology Convergence and Service 2 (1):1–8. doi:10.5121/ijitcs.2012.2101.
   Google Scholar
• Nenava, S., and V. Choudhary. 2013. Hybrid personalized recommendation approach for improving mobile e-commerce. International Journal of Computer Science & Engineering Technology 4 (5):546–52.
   Google Scholar
• Ofran, Y., and H. Margalit. 2006. Proteins of the same fold and unrelated sequences have similar amino acid composition. Proteins 64:275–79. doi:10.1002/prot.20964.
   PubMed Web of Science ®Google Scholar
• Panda, M., and M. R. Patra. 2009. A novel classification via clustering method for anomaly based network intrusion detection system. International Journal of Recent Trends in Engineering 2 (1):1–6.
   Google Scholar
• Pereira, F. C., N. Tishby, and L. Lee. 1993.Distributional clustering of English words. Paper presented at the 31st Annual Meeting on Association for Computational Linguistics (ACL ‘93), Columbus, OH, June 22–26. doi: 10.3115/981574.981598.
   Google Scholar
• Poggi, N., T. Moreno, J. L. Berral, R. Gavaldà, and J. Torres. 2007. Web customer modeling for automated session prioritization on high traffic sites. In Proceedings of the International Conference on User Modeling (UM’07), ed. C. Conati, K. McCoy, and G. Paliouras, LNCS 4511, 450–54. Berlin Heidelberg, Germany: Springer-Verlag. doi:10.1007/978-3-540-73078-1_63.
   Google Scholar
• Schneidman, E., N. Slonim, N. Tishby, R. R. de Ruyter Van Steveninck, and W. Bialek. 2002. Analyzing neural codes using the Information Bottleneck method. Technical report, The Hebrew University, Jerusalem, Israel.
   Google Scholar
• Shim, B., K. Choi, and Y. Suh. 2012. CRM strategies for a small-sized online shopping mall based on association rules and sequential patterns. Expert Systems with Applications 39 (9):7736–42. doi:10.1016/j.eswa.2012.01.080.
   Web of Science ®Google Scholar
• Slonim, N. 2002. The Information Bottleneck: Theory and Applications. PhD Thesis, Hebrew University, Jerusalem, Israel. http://www.cs.huji.ac.il/labs/learning/Theses/Slonim_PhD.pdf.
   Google Scholar
• Slonim, N., N. Friedman, and N. Tishby. 2002. Unsupervised document classification using sequential information maximization. Paper presented at the 25th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR’02), Tampere, Finland, August 11–15 doi: 10.1145/564376.564401.
   Google Scholar
• Slonim, N., R. Somerville, N. Tishby, and O. Lahav. 2001. Objective classification of galaxy spectra using the Information Bottleneck method. Monthly Notices of the Royal Astronomical Society 323 (2):270–84. doi:10.1046/j.1365-8711.2001.04125.x.
   Web of Science ®Google Scholar
• Slonim, N., and N. Tishby. 1999. Agglomerative information Bottleneck. In Advances in Neural Information Processing Systems 12, ed. S. A. Solla, T. K. Leen, and K. Müller, 617–23. Cambridge, MA: MIT Press.
   Google Scholar
• Slonim, N., and N. Tishby. 2000. Document clustering using word clusters via the Information Bottleneck method. Proceedings of SIGIR’00, 208–15. New York, NY: ACM.
   Google Scholar
• Soiraya, B., A. Mingkhwan, and C. Haruechaiyasak. 2008. E-commerce Web site trust assessment based on text analysis. International Journal of Business and Information 3 (1):86–114.
   Google Scholar
• Soliman, T. H. A., M. A. Elmasry, A. R. Hedar, and M. M. Doss. 2012. Utilizing support vector machines in mining online customer reviews. Paper presented at the 22nd International Conference on Computer Theory and Applications (ICCTA’12), Alexandria, Egypt, October 13–15. doi: 10.1109/ICCTA.2012.6523568.
   Google Scholar
• Song, Q., and M. Shepperd. 2006. Mining Web browsing patterns for e-commerce. Computers in Industry 57 (7):622–30. doi:10.1016/j.compind.2005.11.006.
   Web of Science ®Google Scholar
• Su, Q., and L. Chen. 2015. A method for discovering clusters of e-commerce interest patterns using click-stream data. Electronic Commerce Research and Applications 14 (1):1–13. doi:10.1016/j.elerap.2014.10.002.
   Web of Science ®Google Scholar
• Suchacka, G. 2014. Analysis of aggregated bot and human traffic on e-commerce site. In Annals of Computer Science and Information Systems (ACSIS), Vol. 2: Proceedings of the Federated Conference on Computer Science and Information Systems (FedCSIS’14), ed. M. Ganzha, L. Maciaszek, and M. Paprzycki, 1123–30. New York, NY: IEEE. doi: 10.15439/2014F346.
   Google Scholar
• Suchacka, G., and L. Borzemski. 2013. Web server support for e-customer loyalty through QoS differentiation. Transactions on Computational Collective Intelligence (XII):89–107. doi:10.1007/978-3-642-53878-0_5.
   Google Scholar
• Suchacka, G., and G. Chodak. 2016. Using association rules to assess purchase probability in online stores. Information Systems and e-Business Management 15 (3):751–80. doi:10.1007/s10257-016-0329-4.
   Web of Science ®Google Scholar
• Suchacka, G., M. Skolimowska-Kulig, and A. Potempa. 2015a. A k-Nearest Neighbors method for classifying user sessions in e-commerce scenario. Journal of Telecommunications and Information Technology 3:64–69.
   Google Scholar
• Suchacka, G., M. Skolimowska-Kulig, and A. Potempa. 2015b. Classification of e-customer sessions based on Support Vector Machine. In Proceedings of the 29th European Conference on Modelling and Simulation (ECMS’15), ed. V. M. Mladenov, G. Spasov, P. Georgieva, and G. Petrova, 594–600. European Council for Modelling and Simulation. doi: 10.7148/2015-0594.
   Google Scholar
• Suchacka, G., and S. Stemplewski. 2017. Application of neural network to predict purchases in online store. In Advances in Intelligent Systems and Computing (AISC) 524, ISAT’16 - Part IV, ed. Z. Wilimowska, L. Borzemski, A. Grzech, and J. Świątek, 221–33. Cham, Switzerland: Springer. doi:10.1007/978-3-319-46592-0_19.
   Google Scholar
• Tan, P.-N., M. Steinbach, and V. Kumar. 2006. Introduction to data mining. Boston, MA: Pearson Addison-Wesley.
   Google Scholar
• Tishby, N., F. C. Pereira, and W. Bialek. 1999. The Information Bottleneck method. Paper presented at the 37th Annual Allerton Conference on Communication, Control, and Computing, Monticello, IL, September 22–24.
   Google Scholar
• Wang, Y. Y., A. Luse, A. M. Townsend, and B. E. Mennecke. 2015. Understanding the moderating roles of types of recommender systems and products on customer behavioral intention to use recommender systems. Information Systems and e-Business Management 13 (4):769–99. doi:10.1007/s10257-014-0269-9.
   Web of Science ®Google Scholar
• Zhang, X., J. Edwards, and J. Harding. 2007. Personalised online sales using Web usage data mining. Computers in Industry 58 (8–9):772–82. doi:10.1016/j.compind.2007.02.004.
   Web of Science ®Google Scholar
• Zhou, X., J. Wei, and C.-Z. Xu. 2006. Resource allocation for session-based two-dimensional service differentiation on e-commerce servers. IEEE Transactions on Parallel and Distributed Systems 17 (8):838–50. doi:10.1109/TPDS.2006.111.
   Web of Science ®Google Scholar
• Ziv, E., M. Middendorf, and C. H. Wiggins. 2005. An information-theoretic approach to network modularity. Physical Review E 71:046117. doi:10.1103/PhysRevE.71.046117.
   Web of Science ®Google Scholar