search rank

Data Visualization and the OPAC

A chat with Ryan Eby, also an Edward Tufte fan, elicited this line about another reason we continue to struggle with the design of our catalogs:

data isn’t usable by itself

if it was then the OPAC would just be marc displays

And yesterday I was speaking with Corey Seeman about how to measure and use “popularity” information about catalog items. It got me thinking about Flickr’s interestingness metric, which seems to combine the number of times a photo has been “favorited,” viewed, and commented. In a related fashion, I’ve been looking at ways to track the terms people use to find catalog items and use those to help improve search results. A basic form of this is in the OPAC prototype I demonstrated yesterday.

And all of this has me looking forward to Aaron Krowne’s Quality Metrics presentation at code4lib.

Chasing Clicks

Al asked how low I will go to chase traffic. Truth is, I can’t answer. Maisonbisson has had moments of popularity, but it’s hard to know why.

Alexa tells us there are 18 million unique sites on the Web, but…

if you take Alexa’s Top 100,000 sites you’ll find that almost 3 out every 4 clicks are spoken for. In other words, almost 75% of all the traffic on the web goes to the sites in the Top 100K list, leaving the remaining 18 million or so sites to fight over the scraps.

Like the distribution of wealth on the planet, the distribution of traffic on the Web is extremely lopsided. The Top 500 are champagne and caviar. Sites 501 – 100,000 are meat and potatoes. The rest are hungry.

(Link in original, don’t you like that political jab?)