Content-based news recommendation: Comparison of time-based system and keyphrase-based system

Abstract

As internet resources are increasing at an unprecedented speed, users are tired of searching important ones among them. So, what users need and where they can find them are getting more important. 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 content-based news recommendation system for news domain is presented and contents of news articles are represented by words appearing in news articles or their keyphrases. 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.