The Community for Technology Leaders
International Workshop on Challenges in Web Information Retrieval and Integration (2005)
Tokyo, Japan
Apr. 8, 2005 to Apr. 9, 2005
ISBN: 0-7695-2414-1
pp: 128-135
Mei-Ling Shyu , Department of Electrical and Computer Engineering, University of Miami Coral Gables, FL, USA
Choochart Haruechaiyasak , Information Research and Development Division (RDI) National Electronics and Computer Technology Center (NECTEC) Thailand Science Park
Na Zhao , Distributed Multimedia Information System Laboratory, School of Computer Florida International University, Miami, FL, USA
Shu-Ching Chen , Distributed Multimedia Information System Laboratory, School of Computer Florida International University, Miami, FL, USA
ABSTRACT
<p>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.</p>
INDEX TERMS
Association Rule Mining, Collaborative Filtering, Web Data Extraction, Web Log/Navigation Path Analysis.
CITATION
Mei-Ling Shyu, Choochart Haruechaiyasak, Na Zhao, Shu-Ching Chen, "Collaborative Filtering by Mining Association Rules from User Access Sequences", International Workshop on Challenges in Web Information Retrieval and Integration, vol. 00, no. , pp. 128-135, 2005, doi:10.1109/WIRI.2005.14
100 ms
(Ver )