
Machine-Learning RSS Reader based on simple user feedback - zathrus_writer
I&#x27;ve created an RSS Reader that uses machine learning to deconstruct articles marked by the user as liked&#x2F;disliked, scoring article&#x27;s elements (words, categories, authors...) and allowing the user to filter out articles irrelevant to their interests.<p>Users can also order articles by score instead of date, showing the most relevant ones at the top of the list.<p>I&#x27;d be interested to know whether anyone here would like to try this system once it&#x27;s online? Perhaps it could help researchers, skimming lots of articles on their subject every day, or even ordinary people, trying to find a car of their dreams, filtering out the noise of online auctions irrelevant to them.<p>I&#x27;m aware that there&#x27;s some RSS readers that already partially do this, such as NewsBlur, Feeder or Inoreader but they either over-complicate this by requiring users to set up lots of filters manually or to play an RSS-reader-training-cookie-clicker by having them to rate each valid&#x2F;invalid word, one by one.<p>I&#x27;d prefer avoiding comments about RSS decline, if possible please.
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zathrus_writer
A very early test version for you to try is now located at
[http://54.175.218.217](http://54.175.218.217)

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zathrus_writer
Here are some animated screenshots:
[https://imgur.com/a/eSERqFc](https://imgur.com/a/eSERqFc)

