Tae Ho Kang , Young Soo Min , Jae Soo Yoo

The KIPS Transactions:PartD, Vol. 11, No. 3, pp. 717-730, Jun. 2004
10.3745/KIPSTD.2004.11.3.717,   PDF Download:

Abstract

Recently, many researches on the personalization of a web-site have been actively made. The web personalization predicts the sets of the most interesting URLs for each user through data mining approaches such as clustering techniques. Most existing methods using clustering techniques represented the web transactions as bit vectors that represent whether users visit a certain URL or not to cluster web transactions. The similarity of the web transactions was decided according to the match degree of bit vectors. However, since the existing methods consider only whether users visit a certain URL or not, users´ interestingness on the URL is excluded from clustering web transactions. That is, it is possible that the web transactions with different visit purposes or inclinations are classified into the same group. In this paper, we propose an enhanced transaction modeling with interestingness weight to solve such problems and a new similarity measuring method that exploits the proposed transaction modeling. It is shown through performance evaluation that our similarity measuring method improves the accuracy of the web transaction clustering over the existing method.

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