Collaborative filtering by mining association rules from user access sequences Conference

Shyu, ML, Haruechaiyasak, C, Chen, SC et al. (2005). Collaborative filtering by mining association rules from user access sequences . 2005 128-133. 10.1109/WIRI.2005.14

cited authors

  • Shyu, ML; Haruechaiyasak, C; Chen, SC; Zhao, N

fiu authors

abstract

  • 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. © 2005 IEEE.

publication date

  • December 1, 2005

Digital Object Identifier (DOI)

International Standard Book Number (ISBN) 10

  • 0769524141

International Standard Book Number (ISBN) 13

  • 9780769524146

start page

  • 128

end page

  • 133

volume

  • 2005