Which brings me to idea #4.
We’ll extend the history function of the browser. We’ll subject your browsing history to time-series analysis and data mining.
Whereas your favourites – or del.icio.us – is a map of the exceptional sites in your browsing, we’ll make this a map of your regularities. we’ll look for daily cycles, weekly cycles, and more complex ones. I have a series of webcomics i visit every day—they align to a 5/7 frequency.
Then we can go further. My daily routine includes reading boingboing.net and a bunch of other blogs. I visit a load of sites because they were linked to by these blogs. These are echoes of my daily frequency—and maybe these exceptional sites, ones I visit only once, could be used for the equivalent of echo-location?
Perhaps, seeing my rhythms and my echoes, we could see who else resonates with me, sharing the basic rhythms. These other people are near me in my browsing habits, because they follow the same links: that is, they’re echo-located to the same position in browsing-space.
On a more personal level, maybe my browsing could be more assisted. Visiting one site of my daily routine could trigger a cascade of other. Or, perhaps, if I forget a site, I could dig back in my history to see what sites I was visiting regularly last month. Actually, maybe I want to forget some of those.
So what idea #4 really is: It’s a way to make visible my personal map of browsing. I can divide out those sites that I hold with me, with certain rhythms, and those which leap out and make loud noises. Those are the sites I end up putting in my favourites—because really that’s another kind of map entirely (a pataphysical map; one that exerts force on the mapped territory).