Carsten - Thanks for your comment and the link to your post. It is a very good post.

Since I am suggesting the use of click-through rates and not raw traffic, it doesn’t really matter if the site is a low traffic one.

I personally think that manipulation will always exist as long as there is money to be made.

Ralph - You are right that using the toolbar alone would be a suicide. For that reason, I am only suggesting they use click-through data to bias the weights (probabilities) of the votes (links). Measuring all the links in a page (even the invisible ones) the same, is counterintuitive in my opinion.

I agree that manipulation will always exist. The point I am trying to make is that Google need to address this problem algorithmically.

Tom - Thanks for your input. Sorry I forgot to mention that. I think they should combine the click-through data offline at the same time the PageRank is being computed. That way it is query-independent and the search results will come back fast to the user.

I am trying to get my Math up to speed. I see it is a must, in order to understand all these interesting papers I am reading lately.

do you think Google would ever accept a pagerank algorithm created by the search community!?

That is a good point. I am not hoping they’d use it, but more to proof that it can be done. The idea is to push them in the right direction and stop scaring us.
Will - From what I’ve read it seems they are still using the Power Method. It is slow, but does not require changes to the massive adjacency matrix that could result in massive storage/computation issues.

The idea is to factor the click-through rates at the time the PageRank is being computed. That way it is query-independent. Maybe, averaging the click-through rates from the last PageRank update to date, would be a sound approach.

BTW: Happy birthday, Tom!

David H – My suggestion is to use the click-through rates to weight the links on each page. It doesn’t really matter the amount of traffic the page gets. What matters is the distribution of the traffic.

This data can be a bit ambiguous, because the bounce rate would suggest to Google that this is a low quality site, but the time spent on site would suggest high quality.

In that case, they can incorporate the average time on site too. That information is available to them via the toolbar. Thanks for the input.

David D – Corrected, thanks.

Tom F – Thanks for the link.

If you want to read the results of thousands of tests regarding the lastest Google updates, this page will give you valuable information.

http://gototom2.blogspot.com/2006/09/80-of-webmasters-are-wrong-about-seo.html

Also Google has not fixed all the innocent sites that they penalized, many do not even realize they have been penalized yet. We have not heard the last of this.

I hope all’s well with you.

This data can be a bit ambiguous, because the bounce rate would suggest to Google that this is a low quality site, but the time spent on site would suggest high quality.

The problem is abuse of this system would be impossible to track, whereas paid links can be spotted by humans or guessed at by computers.

Tom brings up an interesting point here: “do you think Google would ever accept a pagerank algorithm created by the search community!?”

Earlier I was reading that Jimbo Wales of Wikipedia wants to take on Google in the search engine market and he is relying on the open source community - all of the code will be available. The obvious problem here is people will know the exact inner workings and be able to exploit the system. It was like when Infoseek released all their code, a few people worked out their system and started selling automated rankings on the engine. However, it came to mind that Jimbo would be able to allow people to make their own algorithms and systems. So rather than just suggesting your ideas to Google, you can just take action. If Jimbo can pull something like that off and make it work, I think the search market will be his.

Interesting post. I haven’t thought about eigenvectors in detail for some time. Stochastic convergence is a fun subject though. I’d be very interested in what kind of convergence they are using - I suspect quite a weak form (actually, when I say “very interested” I mean “a little bit interested, but not quite enough to go and read the original papers”).

I’m going to have a think (and Tom and I will discuss - he is too modest about his maths abilities) about how you might model an ‘intelligent surfer’ (there’s an oxymoron for you!).

I think one of the biggest challenges is going to be how you make it query-independent (which is important for computational intensiveness)…. Hmmmmmmm….

Thanks for the thought-provoking post.

While we’re talking about using toolbar data - why not go the whole way and use traffic stats for each page as a factor in the algorithm for how important it is? Some combination of traditional pagerank, modified by how many visits it gets. After all, if a page is getting 10,000 hits a day then if the pagerank algorithm is doing it’s job the page should have a high pagerank (or probability of a random surfer hitting the page).

Trying to use pagerank data on each link on a page (and then also relating that to the toolbar data on the linked-to page) I think unfortunately will cause the algorithm to slow down too much.

Thanks for outing me as some kind of maths genius as well Hamlet - in actual fact Will is much better at maths than I am (although I think we’re both a bit rusty having not used any hardcore maths for a good few years!!).

One last note - do you think Google would ever accept a pagerank algorithm created by the search community!? I mean user generated content is one thing but I think that’s taking it a bit far! We’d know exactly how to rank sites since we wrote the algo