Advanced Techniques in Web Intelligence-2
Web User Browsing Behaviour and Preference Analysis
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Beschreibung
This research volume focuses on analyzing the web user browsing behaviour and preferences in traditional web-based environments, social networks and web 2.0 applications, by using advanced techniques in data acquisition, data processing, pattern extraction and cognitive science for modeling the human actions.
The book is directed to graduate students, researchers/scientists and engineers interested in updating their knowledge with the recent trends in web user analysis, for developing the next generation of web-based systems and applications.
This research volume focuses on analyzing the web user browsing behaviour and preferences in traditional web-based environments, social networks and web 2.0 applications, by using advanced techniques in data acquisition, data processing, pattern extraction and cognitive science for modeling the human actions.
The book is directed to graduate students, researchers/scientists and engineers interested in updating their knowledge with the recent trends in web user analysis, for developing the next generation of web-based systems and applications.
Latest research on the theory and applications of advanced techniques in web usage mining State of the art of web usage mining and web intelligence Written by leading experts in the field
Autor*in
Juan D. Velásquez
Themen in »Advanced Techniques in Web Intelligence-2«
Computational intelligence Machine learning for the web-based systems Mining usage patterns Web intelligence Web personalization Web recommender systems Web usage mining
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From the reviews:
“This volume presents a progression of ideas, starting with data gathering, extending through behavioral characterization and user modeling, and ending with applications. Extensive references to the research literature accompany each chapter, and a rather brief subject index concludes the volume. This book is suitable for researchers and graduate students looking for a survey of current trends in the modeling of web user behavior from the perspective of web data analysis or cognitive science.” (R. M. Malyankar, ACM Computing Reviews, February, 2013)
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