Abstract:
In the present internet has become much more necessary thing to humans and we use it as a way of sharing information
and way of communication. If the networks can identify the user’s intentions, it will be affecting to increase productivity
and personalization. Predicting user intention(s) is interesting and useful for many applications such as threat
identification, imposing restrictions and cashing web details. The aim of this research is to develop a method to predict
user intention using supervised machine learning methods with user’s past historical behaviours. Experiments in this
study used access log on a local server and focused on creating single user prediction and multiuser generalize
prediction models. Experimental models were created based on several multi-classifier algorithms, such as Support
Vector Machine (SVM), Multilayer Perceptron (MLP) and K-Nearest Neighbor (KNN). KNN based models outperform
other used algorithms. Also results in this study show that there is some sort of behavioural patterns for peoples to use
the internet according to the time and the groups they interact
