By Pablo E. Román, Juan D. Velásquez (auth.), Juan D. Velásquez, Vasile Palade, Lakhmi C. Jain (eds.)
This study quantity specializes in studying the net consumer looking behaviour and personal tastes in conventional web-based environments, social networks and net 2.0 purposes, through the use of complex suggestions in information acquisition, info processing, development extraction and cognitive technological know-how for modeling the human activities.
The booklet is directed to graduate scholars, researchers/scientists and engineers attracted to updating their wisdom with the new tendencies in internet consumer research, for constructing the subsequent new release of web-based structures and purposes.
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Extra info for Advanced Techniques in Web Intelligence-2: Web User Browsing Behaviour and Preference Analysis
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. In this case, the nature of the data can be either explicit or implicit, contributed by the end users as they interact with a particular website.
47–51. ACM, New York (2010) 19. : Evaluating the accuracy of implicit feedback from clicks and query reformulations in web search. ACM Trans. Inf. Syst. 25(2) (April 2007) 20. : Semantic outlier analysis for sessionizing web logs. In: Proceedings of the 1st European Web Mining Forum, EWMF 2003, Croatia (2003) 21. : Implicit feedback for inferring user preference: a bibliography. SIGIR Forum 37(2) (September 2003) 2 Web Usage Data Pre-processing 33 22. : Active user-based and ontology-based web log data preprocessing for web usage mining.
10 respectively. 11) The F-measure is a factor that yields the overall evaluation of both metrics, and can be used to decide whether the sessionization algorithm has acceptable results when this measure is higher . However, as Web users change their behavior with respect to a particular website, these values must be evaluated over time to identify the potential concept-drift of the unsupervised classification problem . Precision, recall and F-measure are the most basic metrics that can be computed to evaluate the quality of any sessionization heuristic.
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.)