Comparing and evaluating information retrieval algorithms for news recommendation
Proceedings of the 2007 ACM conference on Recommender systems
Entity categorization over large document collections
Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining
Personalized news recommendation based on click behavior
Proceedings of the 15th international conference on Intelligent user interfaces
Design and implementation of e-journal review system using text-mining technology
ICCCI'10 Proceedings of the Second international conference on Computational collective intelligence: technologies and applications - Volume Part III
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Rapid growth of technologies provides ample ways and mechanisms to provide information to any person about any entity. Knowing what had happened around an individual is an essential requirement in this modern web era. This is achieved with the help of various news service providers such as yahoo, google, your news etc that delivers various news documents to the users irrespective of time and place. Most of the news service providers do not take into the account the users choice or interests for content delivery. Providing all news to all may not be an appropriate one when there is diversified class of audience. Personalization is the key factor that aims at providing the relevant data for the related person. In order to deliver news documents to the exact user, it is most essential to personalize the news documents so that users can have the comfort of getting the news of their preferences or interests. In this paper, we have proposed a classification method named Enhanced Term-Document Frequency (ETF-IDF) method, by which news documents of varied categories are classified and made personalized as per the users interests.