A low-order markov model integrating long-distance histories for collaborative recommender systems
Proceedings of the 14th international conference on Intelligent user interfaces
Collaborative filtering for orkut communities: discovery of user latent behavior
Proceedings of the 18th international conference on World wide web
Towards Privacy Compliant and Anytime Recommender Systems
EC-Web 2009 Proceedings of the 10th International Conference on E-Commerce and Web Technologies
Efficient mining top-k regular-frequent itemset using compressed tidsets
PAKDD'11 Proceedings of the 15th international conference on New Frontiers in Applied Data Mining
A scalable privacy-preserving recommendation scheme via bisecting k-means clustering
Information Processing and Management: an International Journal
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Recent research in mining user access patterns for predicting Web page requests focuses only on consecutive sequential Web page accesses, i.e., pages which are accessed by following the hyperlinks. In this paper, we propose a new method for mining user access patterns that allows the prediction of multiple non-consecutive Web pages, i.e., any pages within theWeb site. Our approach consists of two major steps. First, the shortest path algorithm in graph theory is applied to find the distances between Web pages. In order to capture user access behavior on the Web, the distances are derived from user access sequences, as opposed to static structural hyperlinks. We refer to these distances as Minimum Reaching Distance (MRD) information. The association rule mining (ARM) technique is then applied to form a set of predictive rules which are further refined and pruned by using the MRD information. The proposed approach is applied as a collaborative filtering technique to recommend Web pages within a Web site. Experimental results demonstrate that our approach improves performance over the existing Markov model approach in terms of precision and recall, and also has a better potential of reducing the user access time on the Web.