RSS Readers Part 2

How would Bayesian Tagging work and what would it look like? I put together some conceptual images of what is floating around in my head. I plan on sticking this all into a feature request for RSSOwl.



The stars for each tag represent how much you trust that the rss reader can correctly predict if a news article is part of that tag. When creating a new tag it should sit at 0 stars for awhile. When processing a new items and generating statistical scores for each of the tags the trust level defines the minimum probability that the item must make to be officially tagged. If the rss reader thinks it should be in that tag but the percentage is below the trust level it instead would add it to the "suggested tags list" for that article. This brings us to my next image.



In the image above you can see the list of suggested tags for this article. If the user agrees with one of the suggested tags she can then click that tag to officially tag it. When that happens the rss reader should process the item text for new words to add to the tag and generate new statistical probabilities. Then it should move the tag from the suggested tag list to the tag list. Since a user may want to add many suggested tags to an article it is important not to bring up a dialog box when clicking a suggest tag. That way the user can quickly click the correct suggest tags for each item and move on.

One thing to note in the second image is the fact that at first the tags are hidden under the item description. This is because there may be many suggested and real tags for a item. If each item listed its tags then the item list would become cluttered and hard to read.