Prediction of navigation profiles in a distributed Internet environment through learning of graph distributions

摘要

Collaborative filtering techniques in the Internet are a means to make predictions about the behavior of a certain user based on the observation of former users. Frequently in literature the exploited information is contained in the access-log files of web servers storing requested data objects. However with additional effort on the server side it is possible to register, from which to which data object a client actually navigates. In this article the profile of a user in a distributed web environment will be modeled by the set of his navigation decisions between data objects. Such a set can be regarded as a graph with the nodes being the requested data objects and the edges being the decisions. A method is presented to learn the distribution of such graphs based on distance functions between graphs and the application of clustering techniques. The estimated distribution is used to predict future navigation decisions of new users. Results with randomly generated graphs show properties of the new algorithm. A measure to estimate the prediction quality for observed profiles is presented.