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    News Selection with Topic Modeling

    Fifth BCS-IRSG Symposium on Future Directions in Information Access (FDIA 2013)

    3 September 2013, Granada, Spain

    AUTHORS

    Cagri Toraman

    ABSTRACT

    There are numerous news articles coming to news aggregators and important news are selected to be presented on the front-page. There are two types of news selection for the front-page of news aggregators: personalized and public news recommendation (selection). This study examines public news recommendation that aims to satisfy all users’ interest on the front-page. Public news recommendation is mainly done by meta-features like news popularity. A different approach that exploits the news content is introduced in this work. The main target is to select important (significant) news articles while providing diversification in the selected news topics. A new approach based on topic modeling is developed for this purpose. Results show that it is hard to achieve satisfactory level of precision when content-based public news recommendation is applied. However, precision of topic modeling-based approach is noticeably better than precision of random news recommendation. Topics of selected news are also diversified by using topic modeling.

    PAPER FORMATS

    PDF file PDF Version of this Paper (371kb)