I was a fairly active USENET reader in the early-to-mid nineties. For a while I used tin as my news reader but once the quantity of groups I was regularly reading reached a critical mass, I found the approach of nn more suitable. In the former, I would navigate to a particular newsgroup and if I saw any articles of interest, I'd navigate into each, one-by-one. In the latter, I'd scan the list of articles across all newsgroups and tag those that looked interesting and only then would start to read them.
Recently I've heard seasoned blog readers talking about their blog reading strategies in very similar terms to the way things were done with nn. I would say my current blog reading is more tin-like but I am starting to reach that point where I may have to switch to an nn-like reading strategy.
by : Created on April 10, 2004 : Last modified Feb. 8, 2005 : (permalink)
Mouthful of a title, I know.
During my reading-USENET-via-nn days, I envisaged a news reader that would learn from what I selected and what I didn't select to read and would sort the articles according to how likely it thought I would want to read them.
I didn't know about Bayesian Classification at the time. Now that I do, it seems the perfect technique to use.
I wonder if a similar technique would be useful in prioritizing the reading of blog entries. Admittedly, the signal-to-noise ratio on the blogs I read is considerably higher than USENET but the quantity of blogs I now read makes it potentially useful.
by : Created on April 10, 2004 : Last modified March 28, 2005 : (permalink)