Abstract
Recently, many archival news article collections have been made available to wide public. However, such collections are typically large, making it difficult for users to find content they would be interested in. Furthermore, archived news articles tend to be perceived by ordinary users as having rather weak attractiveness and being obsolete or uninteresting. In this paper, we propose the task of finding interesting content from news archives and introduce two simple methods for it. Our approach recommends interesting content by comparing the information written in the past with the one from the present.
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CITATION STYLE
Hung, I. C., Färber, M., & Jatowt, A. (2018). Towards recommending interesting content in news archives. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 11279 LNCS, pp. 142–146). Springer Verlag. https://doi.org/10.1007/978-3-030-04257-8_13
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