In my spare time recently I’ve been working on a little project with Alex Kirtland to develop a directory site for these various rating and reputation websites that are popping up all over the place these days. As well as a directory, we have a blog and are working on a “gallery” of reputation and rating systems that should be a useful resource for designers of these systems.
Our directory is split into two types of rating sites — those for products and services, and those for fun. In the latter category, check out Rate My Rosetta and Rate My Turban. Of course, you can also rate the rating sites in our directory.
The site is up and running at 101ratings.com. We’re still adding sites to the directory, and if you run one or know one that isn’t listed, there’s a submission form. Please check it out, tell me what you think, and tell your friends.
Thanks also to loyal blog reader Chewxy for helping me out with some tricky web bits.
This article in Wired has a great account of the contest for the Netflix Prize. In late 2006, Netflix offered a US$1m prize for anyone who could come up with a movie recommendation algorithm that performed 10% better than its own. So far, the prize is unclaimed, although the leaders are close on aroud 8.75%.
The Wired article is mainly a profile of one of the contestants, Gavin Potter, who was a solo late entrant and quickly beat some of the bigger teams. He’s currently ranked eighth with an 8.14% improvement. He makes an insightful comment:
“The 20th century was about sorting out supply,” Potter says. “The 21st is going to be about sorting out demand.” The Internet makes everything available, but mere availability is meaningless if the products remain unknown to potential buyers.
What Potter is saying is that in the past few decades, quite a lot of business progress was made on the supply side of things — just in time, outsourcing, supply chain management etc. Now the Internet has released shelf-space constraints and opened up the fabled long tail of demand. However it’s not enough just to have a million products in your catalogue, you also have to have a way to match consumers with products as effectively as possible. This matching is a crucial aspect of a business like Netflix, and is the reason why they’re willing to pay a million dollars for a ‘mere’ 10% improvement in matching quality.
Ionut Alex Chitu of the Google Operating System blog has a good point about web-based software services like Gmail and Google Docs: You often have no choice about whether to use an upgraded version of the software or not. For example, Gmail recently went through a major upgrade that many people were unhappy with for various reasons, but they have no option to keep using the previous version. Contrast this with desktop software — I’m still using MS Office 2002 as I had no real need to upgrade to Office XP or Office 2007.
On the other hand, upgrades of web-based software are automatically compatible with previous versions. If Google Docs updates, all your documents will surely be accessible in the new version. Thus web-based software cannot use the strategy of pushing out new versions with incompatible file formats, to force people to purchase an upgrade.
It’s interesting to see how social media is having effects on political campaigns, and in particular the current US presidential campaigns. I don’t mean the YouTube debates, but rather the fan videos that were made by people not officially attached to a campaign. In case you haven’t already seen them, check out Will.i.am’s pro-Obama fan video, the takeoff anti-McCain video, and the lame pro-Hillary Clinton video (or is it actually anti-Hillary?).
Thanks to YouTube, the campaigns no longer have very strong control over the media messages appearing about them. This can be both a blessing, as in Obama’s case, or a curse, as for McCain and Clinton.
Following on from my previous post about the value of content filters, here’s some more thoughts on how to do better filtering …
Like I said, the big problem is finding the gems amongst all the crap. I find myself continually frustrated with the lack of sophistication of the filters that YouTube and similar sites offer. Basically, you can sort items according to popularity or other people’s star-ratings over various periods of time. But the good or unique stuff doesn’t always make the top-10 lists.
One idea for a better filter is just to have more flexibility in the way that ranked results are presented. Check out this video for example:
It has about 375,000 views, which is a decent number, but the most popular videos have 30 million or more views, so if you just rank all videos by views you’ll have to scroll through dozens of pages of results until you get down to the 300,000 views range. So why not let users define the lower and upper limits of the rankings in their search results? For example if you graph all the videos on YouTube according to their number of views, you’ll probably get the fabled “long tail” distribution. This could be presented to users and they could select a range within the ranking to view:
Another thing that would be nice is to be able to identify videos that are likely to become hits but haven’t reached that status yet. I suspect that the path of total views over time for hit videos follows the typical S shape. If YouTube can track downloads over time, they could identify videos that may be in the acceleration stage of this process:
A more sophisticated approach to this would be to use time-series models to try to forecast future popularity. Unlike, say, stock prices or exchange rates which behave like random walks, I suspect there is a lot of forecastable serial correlation in daily views for any given video. It’d be interesting to see if some simple models can reliably predict which videos are likely to become popular once they’ve been on the site for a little while and accumulated some views.
Another alternative approach would be to try to identify users who are good at identifying popular content early on. Are there some users who consistently gave high ratings to videos before they became highly popular? If so, these users’ ratings could be tracked (secretly, so as not to induce them to manipulate the process) to generate video recommendations.
These are just a few random ideas that I had. It seems that with so much data available, a few simple tools could go a long way to helping people to sort the good from the bad, which is obviously a valuable thing for sites like YouTube.
