A Media Tracking and News Recommendation System

Nowadays, the amount of documents on internet resources is increasing at an unprecedented speed and users are tired of searching important and related ones among enormous amount of documents. Users require a personalized support in sifting through large amounts of available information according to their interests and recommendation systems try to answer this need. In this context, it is crucial to offer user friendly tools that facilitate faster and more accurate access to articles in digital newspapers. In this paper, a time-based recommendation system for news domain is presented. News articles are recommended according to user dynamic and static profiles. User dynamic profiles reflect user past interests and recent interests play much bigger roles in the selection of recommendations. Our recommendation system is a complete content-based recommendation system together with categorization, summarization and news collection modules.

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