Thankfully, Netflix‘s business model does work.  My best guess is Netflix‘s “typical” customer is not as avid a consumer of the over 90,000 titles in its library as are Mark and I.

We’re also big fans of Netflix‘s “Watch Instantly” option, which no doubt cuts down on Netflix‘s shipping costs and allows us to simultaneously watch movies together even when stuck on opposite coasts.  As an added bonus, “Watch Instantly” is very eco-friendly. Hopefully Netflix will soon solve whatever licensing or other challenges limit this viewing option to approximately 12,000 titles.

Netflix‘s current prediction algorithm led us to movies which have become new favorites, including Outsourced.  It’s also interesting to compare “most popular titles” in our shared residences in the Pacific Northwest and in the Shenandoah Valley.  According to Netflix, the top four titles uniquely popular with our neighbors in suburban Seattle are “Ducktales, Vol.1,” “Rab Ne Bana Di Jodi,” “Om Shanti Om,” and “Mansfield Park;” while the top four picks of our neighbors in the the Shenandoah Valley are “Gilmore Girls: Season 1,” “Deadwood: Season 2,” ”The Last Sin Eater,” and “Nature’s Most Amazing Events.”

It’s been very interesting following Netflix‘s crowdsource contest to develop a customer-centric prediction algorithm “which substantially improves  the accuracy of predictions about how much someone is going to love a movie based on their movie preferences.”

Wired covered this competition several times, in February 2008 and last month, in June 2009; TechCrunch much more frequently, as do other websites and bloggers.

Today on TechCrunch:

The Netflix Prize Comes To A Buzzer-Beater, Nailbiting Finish

by Jason Kincaid on July 26, 2009

Who knew statistical computing competitions could be so cut throat? Since we reported on the contest last night, two teams in the Netflix Prize have spent the last few hours jumping back and forth on the Netflix leaderboard as the three-year-long competition ticked into its final moments, with last minute sniping submissions coming from both sides. Finally, the results are in: The Ensemble has managed to come from behind to upset BellKor’s Pragmatic Chaos with a top submission of 10.10% — an improvement of .01% — only 4 minutes before the contest closed.

It’s been a long road to get here. Over the last three years computer science teams around the world have been vying for the Netflix Prize — a competition that invited teams to try to improve on Netflix’s movie recommendation algorithm by 10%, with a reward of $1 million to the best submission. Since then teams have gotten progressively closer to the magical 10% mark, but it wasn’t until last month when a number of top teams joined forces to form BellKor’s Pragmatic Chaos that the barrier was finally broken, with a score of 10.08%. However, their announcement kicked off a 30 day last call period where other teams were invited to make their final submissions.Read More

Then again – the better the algorithm, the bigger the conundrum of “so many options, so little time.”

So – time to move the Roku box I use for “Watching Instantly” into my too rarely used home gym.  While I’m at it, I should make sure the only way I can power the Roku box and TV is by pedaling the stationary bike, or by actually using the eliptical trainer… crowdsource solution, anyone?