Mining association rules between sets of items in large databases
SIGMOD '93 Proceedings of the 1993 ACM SIGMOD international conference on Management of data
A graph distance metric based on the maximal common subgraph
Pattern Recognition Letters
Predicting users' requests on the WWW
UM '99 Proceedings of the seventh international conference on User modeling
Web search behavior of Internet experts and newbies
Proceedings of the 9th international World Wide Web conference on Computer networks : the international journal of computer and telecommunications netowrking
Link prediction and path analysis using Markov chains
Proceedings of the 9th international World Wide Web conference on Computer networks : the international journal of computer and telecommunications netowrking
Towards adaptive Web sites: conceptual framework and case study
Artificial Intelligence - Special issue on Intelligent internet systems
Graph distances using graph union
Pattern Recognition Letters
Data mining for path traversal patterns in a web environment
ICDCS '96 Proceedings of the 16th International Conference on Distributed Computing Systems (ICDCS '96)
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Collaborative filtering techniques in the Internet are a means to make predictions about the behaviour of a certain user based on the observation of former users. Frequently in literature the information that is made use of is contained in the access-log files of Internet 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 Internet 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 beeing 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 will make it possible to predict future navigation decisions of new users. Results with randomly generated graphs show properties of the new algorithm.