Detecting the most probable {it next} page a user is bound to visit inside a website has important practical consequences: it allows to suggest recommendations to the visitors as to which may be the pages of interest to them in a complex website; it is of help for website designers for deciding how to organize the site contents and it is also useful for pre-caching voluminous objects that the user will very probably need. In sum, it helps to customize web contents. In order to achieve that goal a classification, prediction an evaluation cycle has to be performed. Among the several possible alternative technologies we discuss a real use of Bayesian Network representations. The obtained results are commented, compared to other approaches and ...
Following the approach described by Heckerman et al. (Following the approach described by Heckerman...
Consider a website and the surfers visiting its pages. A typical issue of interest, for example whil...
A Bayesian network is a graphical model that encodes probabilistic relationships among variables of ...
Detecting the most probable {it next} page a user is bound to visit inside a website has important p...
Abstract. The accurate prediction of Web navigation patterns has immense com-mercial value as the We...
The immense scale of the web has rendered itself as a huge content repository. Web users seek inform...
[[abstract]]Vocational teachers and students in Taiwan nearly have the amount of people 1,200,000. T...
Bayesian networks are powerful tools for representing relations of dependence among variables of a d...
This master's thesis deals with possible applications of Bayesian networks. The theoretical part is ...
Numerous probability models have been suggested for information retrieval (IR) over the years. These...
Bayesian networks are a very general and powerful tool that can be used for a large number of proble...
As a compact graphical framework for representation of multivariate probabilitydistributions, Bayesi...
Consider a website and the surfers visiting its pages. A typical issue of interest, for example whil...
Bayesian networks are tools that were developed by the Artificial Intelligence and Statistic communi...
We consider the case of surfing within a single large Web site, which is important from the point of...
Following the approach described by Heckerman et al. (Following the approach described by Heckerman...
Consider a website and the surfers visiting its pages. A typical issue of interest, for example whil...
A Bayesian network is a graphical model that encodes probabilistic relationships among variables of ...
Detecting the most probable {it next} page a user is bound to visit inside a website has important p...
Abstract. The accurate prediction of Web navigation patterns has immense com-mercial value as the We...
The immense scale of the web has rendered itself as a huge content repository. Web users seek inform...
[[abstract]]Vocational teachers and students in Taiwan nearly have the amount of people 1,200,000. T...
Bayesian networks are powerful tools for representing relations of dependence among variables of a d...
This master's thesis deals with possible applications of Bayesian networks. The theoretical part is ...
Numerous probability models have been suggested for information retrieval (IR) over the years. These...
Bayesian networks are a very general and powerful tool that can be used for a large number of proble...
As a compact graphical framework for representation of multivariate probabilitydistributions, Bayesi...
Consider a website and the surfers visiting its pages. A typical issue of interest, for example whil...
Bayesian networks are tools that were developed by the Artificial Intelligence and Statistic communi...
We consider the case of surfing within a single large Web site, which is important from the point of...
Following the approach described by Heckerman et al. (Following the approach described by Heckerman...
Consider a website and the surfers visiting its pages. A typical issue of interest, for example whil...
A Bayesian network is a graphical model that encodes probabilistic relationships among variables of ...