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A Survey on Analysis of User Behavior on Digital Market by Mining Clickstream Data

  • Praveen Kumar Padigela
  • R. SugunaEmail author
Conference paper
  • 15 Downloads
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 1090)

Abstract

Data stream mining has emerged as one of the most prominent areas with its applications in various areas like network sensors, stock exchange, meteorological research and e-commerce. Stream mining is potentially an active area in which the data is continuously generated in large amounts which are dynamic, non-stationary, unstoppable, and infinite in nature. One of such streaming data generated with the user browsing tendency is Clickstream data. Analyzing the user online behavior on e-commerce Web sites is helpful in drawing certain conclusions and making specific recommendations for both the users and the electronic commerce companies to improve their marking strategies and increase the transaction rates effectively leading to enhance the revenue. This paper aims at presenting a survey of different methodologies and parameters used in analyzing the behavior of a user through Clickstream data. Little deeper, this article also outlines the methods used so far for clustering the users based on mining their interests.

Keywords

Clickstream Behavior Digital market Collaborative filtering 

References

  1. 1.
    Su, Qiang, and Lu Chen. 2014. A Method For Discovering Clusters Of E-Commerce Interest Patterns Using Click-Stream Data, 1–13. Elsevier.Google Scholar
  2. 2.
    Constantine, J. Aivalis. 2011. Log File Analysis Of E-Commerce Systems. In Rich Internet Web 2.0 Applications, Panhellenic Conference on Informatics. IEEE.Google Scholar
  3. 3.
    Zhao. 2013. Interest Before Liking: Two-Step Recommendation Approaches, 46–56. Elsevier.Google Scholar
  4. 4.
    Anto Praveena, M.D. 2017. A Survey Paper on Big Data Analytics. In International Conference On Information, Communication & Embedded Systems (ICICES). IEEE.Google Scholar
  5. 5.
    Venkatkumar, Iyer Aurobind. 2016. Comparative Study Of Data Mining Clustering Algorithms. In International Conference On Data Science And Engineering ICDE. IEEE.Google Scholar
  6. 6.
    RamakrishnaMurty, M., J.V.R. Murthy, P.V.G.D. Prasad Reddy, Suresh. C. Sapathy. 2012. A survey of Cross-Domain Text Categorization Techniques. In International conference on Recent Advances in Information Technology RAIT-2012. ISM-Dhanabad, IEEE Xplorer Proceedings. 978-1-4577-0697-4/12.Google Scholar
  7. 7.
    Wang, Gang. 2016. Unsupervised Clickstream Clustering for User Behavior Analysis. In Proceedings of the 2016 CHI Conference on Human Factors in Computing Systems. ACM.Google Scholar
  8. 8.
    Abhaysingh. 2017. Predicting Demographic Attributes from Web Usage: Purpose and Methodologies. In International conference on I-SMAC. IEEE. Google Scholar
  9. 9.
    Joshila Grace, L.K. 2011. Analysis of Web Logs And Web User In Web Mining. International Journal Of Network Security & Its Applications (IJNSA), 3 (1), January.Google Scholar
  10. 10.
    Sergio, Herna´ndez. 2016. Analysis of users’ behaviour in structured e-commerce websites. IEEE.Google Scholar
  11. 11.
    Ben Schafer, J. 2007. Collaborative Filtering Recommender Systems, 291–324. ACM DL.Google Scholar
  12. 12.
    Cai, Yi. 2013. Typicality-based Collaborative Filtering Recommendation. IEEE.Google Scholar
  13. 13.
    Ma, H., I. King, and M.R. Lyu. 2007. Effective Missing Data Prediction for Collaborative Filtering. In SIGIR ’07: Proceedings of the 30th annual international ACM SIGIR conference on Research and development in information retrieval. New York, USA: ACM.Google Scholar
  14. 14.
    Hu, Y., Y. Koren, and C. Volinsky. 2008. Collaborative Filtering for Implicit Feedback Datasets. In Proceedings of ICDM ’08. Washington, DC, USA: IEEE Computer Society.Google Scholar
  15. 15.
    Leung, K.W.-T., D.L. Lee, and W.-C. Lee. 2011. Clr: a collaborative location recommendation framework based on co-clustering. In Proceedings of SIGIR ’11. ACM.Google Scholar
  16. 16.
    Chen, Lu, and Qiang Su. 2013. Discovering User’s Interest At E-Commerce Site Using Clickstream Data. Hong Kong: IEEE.CrossRefGoogle Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2020

Authors and Affiliations

  1. 1.Department of Computer ScienceVel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and TechnologyChennaiIndia

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