Abstract
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.
Highlights
With the web2.0 introduced, its use is growing up along with high speed development in infrastructure and services
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
In this paper we are presenting and investigating the new approach based on weighted Association Rule Mining Algorithm and text mining
Summary
With the web2.0 introduced, its use is growing up along with high speed development in infrastructure and services. Opinions of community members used by the Recommender Systems (RS) in order to facilitate people to establish the knowledge possibly to be fascinating to them or pertinent to their desires This will be achieved by drawing on user preferences and filtering the set of possible choices to a lot manageable set. The assignment of advocated system is to recommend things that match a user’s style, so as to assist the user in selecting/purchasing things from a devastating set of selections Such systems have huge importance in applications like e-commerce, subscription primarily based services, info filtering, internet services etc. Online page Recommendation is an energetic application space for information filtering, internet Mining and Machine Learning analysis Another approach is cooperative recommendation that tries to seek out some users who share similar tastes with the given user and recommends websites they prefer to that user.
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