Measuring the Interestingness of News Articles

摘要

An explosive growth of online news has taken place. Users are inundated with thousands of news articles, only some of which are interesting. A system to filter out uninteresting articles would aid users that need to read and analyze many articles daily, such as financial analysts and government officials. The most obvious approach for reducing the amount of information overload is to learn keywords of interest for a user. Although filtering articles based on keywords removes many irrelevant articles, there are still many uninteresting articles that are highly relevant to keyword searches. A relevant article may not be interesting for various reasons, such as the articles age or if it discusses an event that the user has already read about in other articles.