A business model for collaboratively filtered news?

SUMMARY: Amazon.com has demonstrated the power of collaborative filtering when it comes to selling books. But how about a hyper-personalized, collaboratively filtered news offering? The main challenge may be the business model. Would ad revenue be able to cover the cost?

Via 'Wink Search', an entry on Zeevveez's QTSaver blog, I found an interesting review of 'New Ideas in Search (Wink, Gravee)', on Business 2.0's B2Day blog. Erick Schonfeld writes:

(...) Wink is very much a social search engine, since results are based on how other people previously rated and tagged things. The question is: Will a search based on public tags turn up substantially different results than a regular Google search based on link popularity? After all, at their core both are based on humans making their preferences public (one by explicitly tagging a Website with a descriptive keyword, the other by linking to it). (...)

I agree with Erick that one might wonder if Wink can do a better job than Google, considering that both engines rank search results by popularity. (And by the way, when it comes to tagging and searching, del.icio.us does a very good job, too.)

My interest in search is inspired primarily by one use case: hyper-personalized news provision. When it comes to search relevance, I'm convinced that artificial intelligence is the Holy Grail.

So I've been wondering if anyone is working on an Amazon.com for news. RSS feeds rule, tagging is the tool, Google is gool, but the best way to filter news by relevance is by looking at the news preferences of like-minded users.

Think about a collaboratively filtered news offering. If you and I have had very similar patterns of news consumption in the past, and you have already read and rated a particular piece of news, chances are that I will be interested in reading it, too.

There is an important difference between link popularity and collaborative filtering. Link popularity tells us which search results are considered most relevant to a particular search query by "everybody" (that is, anybody who ever published a link or tagged a piece of published content). Collaborative filtering, on the other hand, sorts search results on the basis of what is know about me, compared to people like me.

That's the power of Amazon.com when it comes to selling books.

So perhaps the main challenge with collaboratively filtered news is the business model. It can only work given a critical mass of users. Which means that the service should probably be offered for free on the Internet. Does that mean ads would have to pay for it? And could they?