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March 28, 2012



I dig it, though it's hard to overlook the expectation from years of looking at Techmeme daily that the clusters are ranked by importance and the that the stream is the same content ranked by recency and linked to its respective location in the importance-ranked left bar.

For anyone who hasn't spend years training themselves to have those expectations though, I think I can see what you're saying: clusters help show what's important, the stream captures everything else and sets the expectation that not everything is in a cluster? Generally speaking though, I really appreciate reading about the idea of visual design being used to offset exaggerated negative perceptions about quality from end users.

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Excellent point. UI can indeed be a way to indicate accuracy of inference(or lack of it) on a bunch of results to a user. I would also add workflow design as a means to utilize less than perfect inference algorithms. To quote an example - disclosures in financial research are typically mind numbing to generate, especially one called 'mentioned company' disclosure, which basically requires a report author to state what companies have been spoken about in the document. Apart from being time consuming to read say 50 page of text + tables, it is also difficult to do brute force search on it since IBM could also be called Big Blue and so on. Here we have used classification algorithms to great effect while also structuring the workflow + UI in a manner that minimizes errors in recognition to slip through.

Again, excellent post. More than anything helps seal the case for adding UI design as an essential element while building an inferencing system.

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