As the growing interest of web recommendation
systems those are applied to deliver customized data for their users, we started
working on this system. Generally the recommendation systems are divided into two
major categories such as collaborative recommendation system and content based recommendation
system. In case of collaborative recommendation systems, these try to seek out users
who share same tastes that of given user as well as recommends the websites according
to the liking given user. Whereas the content based recommendation systems tries
to recommend web sites similar to those web sites the user has liked. In the recent
research we found that the efficient technique based on association rule mining algorithm is proposed
in order to solve the problem of web page recommendation. Major problem of the same
is that the web pages are given equal importance. Here the importance of pages changes
according to the frequency
of visiting the web page as well as amount of time user spends on that page. Also
recommendation of newly added web pages or the pages that are not yet visited by
users is not included in the recommendation set. To overcome this problem, we have used the web
usage log in the adaptive association rule based web mining where the association rules were applied to personalization.
This algorithm was purely based on the Apriori data mining algorithm in order to
generate the association rules. However this method also suffers from some unavoidable
drawbacks. In this paper we are presenting and investigating the new approach based
on weighted Association Rule Mining Algorithm and text mining. This is improved
algorithm which adds semantic knowledge to the results, has more efficiency and
hence gives better quality and performances as compared to existing approaches.
Cite this paper
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