Towards Recommending Interesting Content in News Archives
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.
KeywordsNews archive Interestingness Recommender systems
This research was supported by MEXT grants (#17H01828; #18K19841; #18H03243).
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