Log Mining to Improve the Performance of Site Search

Authors:
Gui-Rong Xue;Hua-Jun Zeng;Zheng Chen;Wei-Ying Ma;Chao-Jun Lu
Affiliations:
-;-;-;-;-
Venue:
WISEW '02 Proceedings of the Third International Conference on Web Information Systems Engineering (Workshops) - (WISEw'02)
Year:
2002

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Abstract

Despite of the popularity of global search engines,people still suffer from low accuracy of site search. Theprimary reason lies in the difference of link structuresand data scale between global Web and website, whichleads to failures of traditional re-ranking methods suchas HITS, PageRank and DirectHit. This paper proposes anovel re-ranking method based on user logs withinwebsites. With the help of website taxonomy, we mine forgeneralized association rules and abstract accesspatterns of different levels. Mining results aresubsequently used to re-rank the retrieved pages. One ofthe advantages of our mining algorithm is that it resolvesthe diversity problem of user's access behavior anddiscovers general patterns. Experiment shows that theproposed method outperforms keyword-based method by15% and DirectHit by 13% respectively.