After my post about Personalized Megite, I got taken to task by both Gabe Rivera from Memeorandum and Nik Cubrilovic from OmniDrive – two developers who have had a lot of experience trying to develop such systems. As Gabe wrote in Scoble’s comments:
“I agree with Nik that there’s a huge technical chasm to cross before a “personalized meme tracker” gets really useful. I think progress I make on the memeorandum engine is approaching that, but it’s still far off enough that I’ll pass on hyping it for now.”
Gabe then proceeded to give me an earful in a Skype conversation about the issue 😉 I was also interested to read Greg Linden‘s thoughts on the matter, as he is another very smart developer with experience in this domain. Actually Greg seemed to like my suggestion of introducing clustering to Findory, which would definitely get me using it more.
Because let’s face it, Personalization + Clustering is the next big step in RSS. If 2005 was about Aggregation, then 2006 is all about Filtering.
Nik wrote up his thoughts today, in a post entitled Memetracking Attempts at Old Issues. While he mentions lack of link data as being an issue, it seems to me the crux of the problem is this:
“generating a personal view of the web for each and every person is computationally expensive and thus does not scale, at all.”
He goes on to say that “this is why you don’t have personalized Google results – we just don’t have the CPU cycles to care about you.”
So it’s mainly a computational and scaling problem. Damn hardware.
Nevertheless, there is a big demand for personalized clustering – among the edge cases, it must be said. And Megite and TailRank are both trying to capture that demand, which to be frank I’m very pleased about. I understand why Gabe and Nik don’t want a bar of it, but there are lots of squeezed bloggers out there who are desperate for a good RSS filtering solution. The first web app that solves this, or at least gives me decent filtered RSS feeds, is going to get my business for sure.
Originally published on ReadWriteWeb (archived copy)