Web Usage Mining as a Tool for Personalization: A Survey
User Modeling and User-Adapted Interaction
PageCluster: Mining conceptual link hierarchies from Web log files for adaptive Web site navigation
ACM Transactions on Internet Technology (TOIT)
Mining Web Log Sequential Patterns with Position Coded Pre-Order Linked WAP-Tree
Data Mining and Knowledge Discovery
Toward trustworthy recommender systems: An analysis of attack models and algorithm robustness
ACM Transactions on Internet Technology (TOIT)
Personalized recommendation with adaptive mixture of markov models
Journal of the American Society for Information Science and Technology
Data mining for web personalization
The adaptive web
COWES: clustering web users based on historical web sessions
DASFAA'06 Proceedings of the 11th international conference on Database Systems for Advanced Applications
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The tremendous growth in volume of web usage data results in the boost of web mining research with focus on discovering potentially useful knowledge from web usage data. This paper presents a new web usage mining process for finding sequential patterns in web usage data which can be used for predicting the possible next move in browsing sessions for web personalization. This process consists of three main stages: preprocessing web access sequences from the web server log, mining preprocessed web log access sequences by a tree-based algorithm, and predicting web access sequences by using a dynamic clustering-based model. It is designed based on the integration of the dynamic clustering-based Markov model with the Pre-Order Linked WAP-Tree Mining (PLWAP) algorithm to enhance mining performance. The proposed mining process is verified by experiments with promising results.