Using predictive prefetching to improve World Wide Web latency
ACM SIGCOMM Computer Communication Review
Discovering the set of fundamental rule changes
Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining
Discovering calendar-based temporal association rules
Data & Knowledge Engineering - Special issue: Temporal representation and reasoning
A Decision-Theoretic Approach for Pre-sending Information on the WWW
PRICAI '98 Proceedings of the 5th Pacific Rim International Conference on Artificial Intelligence: Topics in Artificial Intelligence
Web usage mining: discovery and applications of usage patterns from Web data
ACM SIGKDD Explorations Newsletter
WhatNext: A Prediction System for Web Requests using N-gram Sequence Models
WISE '00 Proceedings of the First International Conference on Web Information Systems Engineering (WISE'00)-Volume 1 - Volume 1
A Data Mining Algorithm for Generalized Web Prefetching
IEEE Transactions on Knowledge and Data Engineering
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
Building Association-Rule Based Sequential Classifiers for Web-Document Prediction
Data Mining and Knowledge Discovery
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
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With the rapid development of E-commerce, the topic of mining and predicting users' navigation patterns has attracted significant attention due to the wide applications like personalized services in E-commerce. Although a number of studies have been done on this topic, few of them take into account the temporal property for web user's navigation patterns. In this paper, we propose a novel method named Temporal N-Gram (TN-Gram) for constructing prediction models of Web user navigation by considering the temporality property in Web usage evolution. Moreover, three kinds of new measures are proposed for evaluating the temporal evolution of navigation patterns under different time periods. Through experimental evaluation on both of real-life and simulated datasets, the proposed TN-Gram model is shown to outperform other approaches like N-gram modeling in terms of the prediction precision, in particular when the web user's navigating behavior changes with temporal evolution.