What does your web browser think you like?

Many modern browsers, including Google Chrome, have a nifty predictive-text feature in the address bar.  You start typing, and it predicts what you’re trying to say. This works even after a single letter. The results are shaped to each individual based on one’s browsing and search histories.

So, I got to thinking: which web sites does Chrome predict for me for each letter of the alphabet?

Here’s what I found:

a – amazon.com (I recently got an Amazon Prime subscription, and it’s pretty awesome)

b – blurity.com

c – classic.wunderground.com (I like the classic interface much better than the “normal” one)

d – digg.com

e – ebay.com (A bit of a surprise, as I haven’t bought or sold anything on eBay in a while)

f – facebook.com

g – goaliestore.com

h – hipmunk.com (Best travel search site at the moment)

i – istockphoto.com (I sell some stock photos here. I also seem not to visit many other “i” sites)

j – join.me (I must not visit many “j” sites either)

k – keacher.com

l – linkedin.com

m – maps.google.com

n – news.ycombinator.com

o – okcupid.com (Still searching for love!)

p – paypal.com (About 30% of Blurity purchases are via PayPal)

q – qrz.com (Very surprised by this one — I think I’ve been here once in the past year, and the site isn’t even all that useful)

r – reddit.com

s – slashdot.org

t – theonion.com

u – uspto.gov (Google is better for searching patents, and freepatentsonline.com is better for viewing them, but only here can I view the PAIR data)

v – vail.com

w – wellsfargo.com

x – xkcd.com

y – youtube.com

z – zappos.com (Per my records, the last time I bought something from Zappos was August 2007)

 

Based on this data, what picture can we draw of me?  Maybe that I’m a tech-savvy engineer who likes to travel, laugh, keep up with friends, and buy and sell things on the internet?

The problem with this data is that it is not directly correlated to the frequency of visits. For that, it’s more useful to look at Chrome’s “Most Visited” sites, visible when a new empty tab is created.

For me, the sites are:

• reddit.com
• okcupid.com
• facebook.com
• news.ycombinator.com
• cnn.com
• blurity.com
• craigslist.org
• wellsfargo.com

Curiously, two of the entries on this list (CNN and Craigslist) were absent on the predictive list. This gets at a second problem with predictive input: it doesn’t work as well when there are multiple sites that start with the same letter or sequence of letters.

I wonder how self-reinforcing these sets of sites are. That is, am I more likely to continue to visit them because the effort required to change is higher than to not change?  It’s possible.

CNN in particular might be an example of that. Most of what they publish comes from the wires, so I could just as easily make something like the Denver Post or Reuters my hard-news mainstay. In fact, I’ve done exactly that on my phone. Still, the motivation to switch on my desktop computers is lacking.

It should be interesting to see how this changes over time. I expect that Hacker News, Wells Fargo, and Blurity will stay on the list for the foreseeable future. OkCupid may drop out if I manage to find that special guy. And Facebook? It’s an unknown. Will I still care about it in four years? Not sure. The next shiny thing might have come to the fore by then.