Silk from a sow's ear: extracting usable structures from the Web
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
The World-Wide Web: quagmire or gold mine?
Communications of the ACM
Mining generalized association rules
Future Generation Computer Systems - Special double issue on data mining
The anatomy of a large-scale hypertextual Web search engine
WWW7 Proceedings of the seventh international conference on World Wide Web 7
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
ACM SIGKDD Explorations Newsletter
Mining web logs for prediction models in WWW caching and prefetching
Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining
Effective personalization based on association rule discovery from web usage data
Proceedings of the 3rd international workshop on Web information and data management
IEEE Transactions on Knowledge and Data Engineering
Proxy Cache Algorithms: Design, Implementation, and Performance
IEEE Transactions on Knowledge and Data Engineering
Clustering Web Sessions by Sequence Alignment
DEXA '02 Proceedings of the 13th International Workshop on Database and Expert Systems Applications
Analysis of navigation behaviour in web sites integrating multiple information systems
The VLDB Journal — The International Journal on Very Large Data Bases
Relational Markov models and their application to adaptive web navigation
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
Knowledge discovery from users Web-page navigation
RIDE '97 Proceedings of the 7th International Workshop on Research Issues in Data Engineering (RIDE '97) High Performance Database Management for Large-Scale Applications
Discovery of Interesting Association Rules from Livelink Web Log Data
ICDM '02 Proceedings of the 2002 IEEE International Conference on Data Mining
webSPADE: A Parallel Sequence Mining Algorithm to Analyze Web Log Data
ICDM '02 Proceedings of the 2002 IEEE International Conference on Data Mining
Web usage mining: discovery and applications of usage patterns from Web data
ACM SIGKDD Explorations Newsletter
A Web page prediction model based on click-stream tree representation of user behavior
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
A New Graph-Based Evolutionary Approach to Sequence Clustering
ICMLA '05 Proceedings of the Fourth International Conference on Machine Learning and Applications
Mining longest repeating subsequences to predict world wide web surfing
USITS'99 Proceedings of the 2nd conference on USENIX Symposium on Internet Technologies and Systems - Volume 2
REFEREE: an open framework for practical testing of recommender systems using ResearchIndex
VLDB '02 Proceedings of the 28th international conference on Very Large Data Bases
Empirical analysis of predictive algorithms for collaborative filtering
UAI'98 Proceedings of the Fourteenth conference on Uncertainty in artificial intelligence
Integrating knowledge flow mining and collaborative filtering to support document recommendation
Journal of Systems and Software
Knowledge flow-based document recommendation for knowledge sharing
KSEM'07 Proceedings of the 2nd international conference on Knowledge science, engineering and management
Mining group-based knowledge flows for sharing task knowledge
Decision Support Systems
Journal of the American Society for Information Science and Technology
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Predicting the next request of a user has gained importance as Web-based activity increases in order to guide Web users during their visits to Web sites. Previously proposed methods for recommendation use data collected over time in order to extract usage patterns. However, these patterns may change over time, because each day new log entries are added to the database and old entries are deleted. Thus, over time it is highly desirable to perform the update of the recommendation model incrementally. In this paper, we propose a new model for modeling and predicting Web user sessions which attempt to reduce the online recommendation time while retaining predictive accuracy. Since it is very easy to modify the model, it is updated during the recommendation process. The incremental algorithm yields a better prediction accuracy as well as a shorter online recommendation time. A performance evaluation of Incremental Click-Stream Tree model over two different Web server access logs indicate that the proposed incremental model yields significant speed-up of recommendation time and improvement of the prediction accuracy.