By Pablo E. Román, Juan D. Velásquez (auth.), Juan D. Velásquez, Vasile Palade, Lakhmi C. Jain (eds.)
This study quantity specializes in examining the net consumer searching behaviour and personal tastes in conventional web-based environments, social networks and net 2.0 purposes, through the use of complicated suggestions in information acquisition, facts processing, development extraction and cognitive technological know-how for modeling the human activities.
The publication is directed to graduate scholars, researchers/scientists and engineers attracted to updating their wisdom with the new tendencies in internet person research, for constructing the subsequent new release of web-based platforms and functions.
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Extra info for Advanced Techniques in Web Intelligence-2: Web User Browsing Behaviour and Preference Analysis
The merging process is usually related to the fact that information might be distributed over several Web servers, and a synchronization process must be performed. Once the data is cleaned and merged, the process of user session identification (or sessionization) is fundamental for most of Web usage mining applications. For this, several methods have been proposed, ranging from basic heuristics to complex optimization formulations. 3. Applications regarding how to collect and process information about the feedback of Web users is presented.
Discov. 6(1), 61–82 (2002) 30. : A privacy control theory for online environments. In: Proceedings of the 42nd Hawaii International Conference on System Sciences, HICSS 2009, pp. 1–10. IEEE Computer Society, Washington, DC (2009) 31. : Estimation of viewer’s response for contextual understanding of tasks using features of eye-movements. In: Proceedings of the 2010 Symposium on Eye-Tracking Research & Applications, ETRA 2010, pp. 53–56. ACM, New York (2010) 32. : A web usage mining framework for mining evolving user profiles in dynamic web sites.
42]). In this chapter a review of current psychological models of the decision-making process are presented. Web usage mining is the analysis of web users’ browsing activity by means of machine learning techniques. Accordingly, web usage patterns can be used for analyzing web user preferences. The process of knowledge discovery in databases (KDD)  consists of data pre-processing (filtering and transformation), data mining, and interpretation and evaluation. Once data have been consolidated (WebWarehouse) , specific machine learning algorithms are applied in order to extract patterns regarding the usage of the web site.
Advanced Techniques in Web Intelligence-2: Web User Browsing Behaviour and Preference Analysis by Pablo E. Román, Juan D. Velásquez (auth.), Juan D. Velásquez, Vasile Palade, Lakhmi C. Jain (eds.)