Intelligent Recommendation System using Semantic information for Web Information Retrieval (original) (raw)
Due to increase in current websites, the web users have been offered with numerous choices and because of this there exists an inability in decision making by the web user while surfing the web. Thus, the user needs the effective and useful suggestion or recommendation for accessing the website efficiently. Therefore, the recommendation results are very useful for the user by handling the website in an efficient way. The core technique of the webpage recommendation rules is prediction and learning process. These processes are used for appraising what users would like to view in the future website and learn the users' behaviors. In particular, this predicting and learning process can suggest an interesting item from the huge set of item based on the knowledge obtained about an active user. The proposed system predicts user navigational preferences from their previous activities and those learned pattern is used for recommending more preferable web sites to web users. Before recommending the pages to the user a collaborative filtering mechanism has been used to refer the historic visiting preferences of a user with other users of having the same interest.
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