The week's pick
Reseach Article
Analysis of Online User Behavior Detection Methodologies and its Evaluation
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 161 - Number 3 |
| Year of Publication: 2017 |
| Authors: Dhanashree Deshpande, Shrinivas Deshpande |
10.5120/ijca2017913126
|
Dhanashree Deshpande, Shrinivas Deshpande . Analysis of Online User Behavior Detection Methodologies and its Evaluation. International Journal of Computer Applications. 161, 3 ( Mar 2017), 1-5. DOI=10.5120/ijca2017913126
Abstract
With the increasing use of internet, users are accessing information and services easily through various media like social communication, multimedia content, online shopping and banking services etc. It becomes challenging task to accurately identify and differentiate normal and suspicious user behavior. Various businesses need information of next user behavior prediction to enhance their service quality. This paper gives the analysis of online user behavior detection and prediction. Various user behaviors identification methods are compared and analyzed. Their parameters are considered and improvements are suggested. The proposed methodology describes anomalous user behavior detection system. The principal component analysis is the feature extraction method used to detect and differentiate normal and anomalous user behavior.
References
- Mette Skov, Peter Ingwersen, Denmark, “Museum web search behavior of special interest visitors”, Library & Information Science Research, Elsevier, vol. 36, pp. 91-98, 20 May 2014.
- Ondrej Kassak, Michal Kompan, Maria Bielikova, “Student behavior in a web-based educational system: Exit intent prediction”, Engineering Applications of Artificial Intelligence, Elsevier, vol. 51, pp. 136-149, 2 Feb 2016.
- Xiaowei Zhu, Shaochun Wu, Guobing Zou, Shanghai, “User Behavior Detection for Online Survey via Sequential Pattern Mining”, 5th International Conference on Instrumentation and Measurement, Computer, Communication and Control, IEEE, 978-1-4673-7723-2/15, pp no. 493-497, 2015.
- Hasan Al Maruf, Nagib Meshkat, Mohammed Eunus Ali, Jalal Mahmud , “Human behavior in different social medias : A case study of Twitter and Disqus”, IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ISBN 978-1-4503-3854-7/15/08, pp. 270-273, 2015.
- Seyed Morteza Ghavami, Masoud Asadpour, Javad Hatami, Mohammad Mahdavi, Iran, “Facebook User’s Like Behavior Can Reveal Personality”, 7th International Conference on Information and Knowledge Technology, IEEE, 978-1-4673-7485-9/15, IKT2015.
- Saeideh Alimolaei, Iran, “An Intelligent system for User Behavior detection in Internet Banking”, 4th Iranian Joint Congress on Fuzzy and Intelligent Systems (CFIS), IEEE, 978-1-4673-8545-9/15, 2015.
- Sergio Duarte Torres, Ingmar Weber, Djoerd Hiemstra, University of Twente, “Analysis of Search and Browsing Behavior of Young Users on the Web”, ACM Transactions on the Web, 1559-1131/2014, vol. 8, no. 2, article 7, pp. 7:1 to 7:54, March 2014.
- Christopher P. Holland, Gordon D. Mandry, “Online Search and Buying Behaviour in Consumer Markets”, 46th Hawaii International Conference on System Sciences, IEEE, 1530-1605/12, pp. 2918-2927, 2013.
Index Terms
Keywords