TY - GEN
T1 - Collaborative filtering by mining association rules from user access sequences
AU - Shyu, Mei Ling
AU - Haruechaiyasak, Choochart
AU - Chen, Shu Ching
AU - Zhao, Na
N1 - Copyright:
Copyright 2008 Elsevier B.V., All rights reserved.
PY - 2005
Y1 - 2005
N2 - 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 the Web 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.
AB - 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 the Web 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.
KW - Association rule mining
KW - Collaborative filtering
KW - Web data extraction
KW - Web log/navigation path analysis
UR - http://www.scopus.com/inward/record.url?scp=33845357693&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=33845357693&partnerID=8YFLogxK
U2 - 10.1109/WIRI.2005.14
DO - 10.1109/WIRI.2005.14
M3 - Conference contribution
AN - SCOPUS:33845357693
SN - 0769524141
SN - 9780769524146
T3 - Proceedings - International Workshop on Challenges in Web Information Retrieval and Integration, WIRI'05
SP - 128
EP - 133
BT - Proceedings - International Workshop on Challenges in Web Information Retrieval and Integration, WIRI'05
T2 - International Workshop on Challenges in Web Information Retrieval and Integration, WIRI'05
Y2 - 8 April 2005 through 9 April 2005
ER -