By Pablo E. Román, Juan D. Velásquez (auth.), Juan D. Velásquez, Vasile Palade, Lakhmi C. Jain (eds.)
This learn quantity makes a speciality of examining the net person looking behaviour and personal tastes in conventional web-based environments, social networks and internet 2.0 functions, by utilizing complex thoughts in info acquisition, facts processing, trend extraction and cognitive technological know-how for modeling the human activities.
The ebook is directed to graduate scholars, researchers/scientists and engineers drawn to updating their wisdom with the hot developments in internet consumer research, for constructing the subsequent new release of web-based platforms and functions.
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Additional info for Advanced Techniques in Web Intelligence-2: Web User Browsing Behaviour and Preference Analysis
All other ocular behavior types last for a shorter period of time, and do not provide enough time for the suspect to focus on the information that is being presented. Some of the traditional eye tracking techniques are based on scleral lens analysis , the evaluation of photo and video oculography [3, 9], and the reflex of the cornea and the center of the pupil based on video analysis . The latter approach is among the most used for current eye tracking research. This method consists of an infrared camera located under the computer screen, with a image-processing software to locate and identify the reflex of the cornea and the pupil center.
In: Proceedings of the 3rd International Workshop on Web Information and Data Management, WIDM 2001, pp. 9–15. ACM, New York (2001) 29. : Discovery and evaluation of aggregate usage profiles for web personalization. Data Min. Knowl. 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.
Personalization of user profiles for content-based music retrieval based on relevance feedback. In: Proceedings of the Eleventh ACM International Conference on Multimedia, MULTIMEDIA 2003, pp. 110–119. ACM, New York (2003) 14. : Capturing multiple interests in news video retrieval by incorporating the ostensive model. In: PersDB 2008, 2nd International Workshop on Personalized Access, Profile Management, and Context Awareness: Databases, Electronic Proceedings, pp. 48–55 (2008) 15. : Evaluating the implicit feedback models for adaptive video retrieval.
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.)