From user access patterns to dynamic hypertext linking
Proceedings of the fifth international World Wide Web conference on Computer networks and ISDN systems
Bayesian classification (AutoClass): theory and results
Advances in knowledge discovery and data mining
SpeedTracer: a Web usage mining and analysis tool
IBM Systems Journal
Towards adaptive Web sites: conceptual framework and case study
WWW '99 Proceedings of the eighth international conference on World Wide Web
Personalization on the Net using Web mining: introduction
Communications of the ACM
Web mining for web personalization
ACM Transactions on Internet Technology (TOIT)
Discovering Web Access Patterns and Trends by Applying OLAP and Data Mining Technology on Web Logs
ADL '98 Proceedings of the Advances in Digital Libraries Conference
Web usage mining: discovery and applications of usage patterns from Web data
ACM SIGKDD Explorations Newsletter
Data mining for path traversal patterns in a web environment
ICDCS '96 Proceedings of the 16th International Conference on Distributed Computing Systems (ICDCS '96)
Letizia: an agent that assists web browsing
IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 1
Avaliação comparativa de algoritmos de personalização para direcionamento de conteúdo
CLIHC '05 Proceedings of the 2005 Latin American conference on Human-computer interaction
Mining personalization interest and navigation patterns on portal
PAKDD'07 Proceedings of the 11th Pacific-Asia conference on Advances in knowledge discovery and data mining
RecoMap: an interactive and adaptive map-based recommender
Proceedings of the 2010 ACM Symposium on Applied Computing
An integrated model for next page access prediction
International Journal of Knowledge and Web Intelligence
Information Systems Frontiers
| Hi-index | 0.00 |
The explosive growth of the web is at the basis of the great interest into web usage mining techniques in both commercial and research areas. In this paper, a web personalization strategy based on pattern recognition techniques is presented. This strategy takes into account both static information, by means of classical clustering algorithms, and dynamic behavior of a user, proposing a novel and effective re-classification algorithm. Experiments have been carried out in order to validate our approach and evaluate the proposed algorithm.