Web personalization is the process of customizing a web site to the needs of each specific user or set of users, taking advantage of the knowledge acquired through the analysis of the user's navigational behavior. Personalized recommendation by predicting user-browsing behavior using association-mining technology has gained much attention in web personalization research area. However, the resulting association patterns did not perform well in prediction of future browsing patterns due to the low matching rate of the resulting rules and users' browsing behavior. In this paper, we extend the traditional association rule problem by allowing a weight to be associated with each item in a transaction to reflect the interest/intensity of each item within the transaction. In turn, this provides us with an opportunity to associate a weight parameter with each item in a resulting association rule. We assign a significant weight to each page based on the time spent by user on each page and visiting frequency of each page, taking in to account the degree of interest instead of binary weighting. We present new personalized recommendation method base on the proposed weighted association-mining technique. We show, through experimentation on real data set that this approach results in more objective and representative predictions and shows a significant improvement in the recommendation effectiveness in comparison to the traditional association rule approaches.
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