Yahya AlMurtadha — новинки

today the problem is not the availability of the information but how to get the related information. A personalized information filtering system must be able to tailor to current interests of the user and to adapt as they change over time. This research has proposed a content-based personal information system that learns the user preferences by analyzing the content of the document and building the user profile. The proposed filtering system monitors a stream of incoming documents to deliver only those matches the user profiles. This system is called RePLS; an agent-based Reinforcement Profile Learning System with adaptive information filtering. The agent approach is used because of its autonomous and adaptive capabilities to perform the filtering. The core of this system is an improved term weighting method which is called “Purity term weighting” to measure the importance of the most suitable terms represented in each profile. The top selected terms are then used to filter the incoming documents to the learned user profiles.

Web Recommendation system attempts to predict the user next browsing activity then recommend the web pages items that are likely to be of interest to the user. The ability of predicting the next visited pages and recommending it to the short term navigation user (anonymous user) is highly needed. This research focuses on improving the prediction of the next visited web pages and introduces them to current anonymous user. An enhanced classification algorithm is used to assign the current anonymous user to the best web navigation profile. As the users’ interests change over time, the recommender system has the ability to modify the current web navigation profiles and keep them updated. These adaptive profiles help the prediction engine to predict and then recommend the next visited pages to the current user in an accurate manner.