For those that don’t know, collaborative filtering is a method of making suggestions for other products, based on your previous shopping habits. It is used by sites/web-apps, like Netflix, Pandora Radio, and Amazon, and, I think, Ulik, and mostly based on user-generated content.

Just working it out logically, you could say several things about user-generated content:

1. there’s a lot of it, but attention is limited to a few leading sites
2. not all users are equal, there are demographic, emotional, intelligence, and other factors that affect how users vote.
3. users are cheap, which also sometimes means that you get what you paid for.

I’m personally not a fan of user-generated content, at least on a massive scale, because of some of the things in that list.

Alternative is the expert-based method, which means that expert-critics analyse a product and give it a rating. It is not often used in a collaborative setting, meaning that it makes suggestions for other products, i.e. Rotten Tomatoes or Metacritic would be sites collect expert input, but don’t, afaik, suggest other matching movies.

The most famous example of an expert-based collaborative filtering system is Pandora Radio, which is built on top of the Music Genome Project, a collection of 50-or-so music-experts that analyse music and assign attributes to it. Those attributes can then be used to match songs. Users’ input isn’t ignored, they can vote on songs, which affects their future track-lists.

A few characteristics of expert-based systems are:

1. They entail significant wage-costs for employees that have invested in their expertise. Counter this against the possible income of a service like Pandora—advertising & referral-fees—and there could be a discrepancy.
2. They cannot rate as much content, as quickly, as a more user-generated system could.
3. They, on the other hand, maintain a consistent quality, that is unmatched by the varying quality that comes out of user-input.

I’m personally much more of a fan of an expert-based system, but sceptical of its economic merit, looking just at point 1.

Most systems seem to be orientated at users mainly, which, if you have a dataset like Netflix’s, is a smart way to go about things. There are some limitations that that entails, as the Netflix prize has revealed, namely that it cannot account for “strange” films like “Napoleon Dynamite,” and that it doesn’t take into account any user-based information, such as demographics or mood.

What do you think, audience? Knowing that users are cheap but a-plenty (but also overwhelmed with competing attention-buckets), and experts are few and expensive, is the solution to still go the user-generated route and try to make that work? In my opinion, expert-based systems require different business models than are popular online these days. You cannot get away with charging nothing, expecting users to magically click your advert, and hope to pay those university-educated experts. That, or, the margins for your products have to be so high (e.g. insurance & travel), to make such a system work (not that I think collaborative filtering and insurance really make that much sense—”give me the insurance radio-station please!“… eh no.)

Enjoy the weekend!
Vincent

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