Our review template asks people to select what type of user they are of the product, so for Digital Cameras for example, they can select whether they are a Casual User, Professional, Hobbyist/ Enthusiast, and so on (or add their own). We then aggregate this data and allow our retailers to add a ’social navigation’ component to their site. We also aggregating this data and enabling users to narrow results on reviews based on their user profile on our own product review site Buzzillions.com

By the way Joshua, I discovered your site after hearing you speak at SNAP earlier this week, and I’m really glad I did. Great job and extremely helpful - I’m looking forward to your book.

I like the Ramones, Devo, and New Order.

So:
New Order fans like Shriekback.
Ramones fans like Talking Heads.
Devo fans also like Talking Heads.
Talking Heads fans like Shriekback.

That’s one first order point for Shriekback, plus two second-order points for Shriekback.

So Shriekback can be recommended to me.

The current Personalized Recommendations page seems to say “We recommend X because you bought Y” for every item, but it may well be leveraging aggregate person-based data as well. There’s no reason why both systems couldn’t be combined into a single engine. I recall it did so in the past (I ever read a white paper on it), but maybe they’ve decided to lean more towards item-based filtering — or at least they are showing that aspect to their customers, since it’s a little easier to understand.

All this being said, I agree that actually *showing* people who are allegedly “like you” is a lot different than simply using that data behind the scenes to make suggestions.

Part of the “black art” of person-based collaborative filteting is that it is NOT about finding another single person very much like you, but that it aggregates thousands of people loosely similar to you. This is hard for many people to understand, which is why they often simply resort to displaying “you are like these people” instead of displaying all of the complex math used to create your profile and recommendations.

For example, a good collab filter engine might look at the fact that I like The Ramones and Devo and, instead of finding another person or group of people who like both of those two bands, it might look further, to 2nd and 3rd or 4th degrees of “like”: Ramones fans also like Talking Heads. Devo fans like Talking Heads. Then it sees that Talking Heads fans like Shriekback. So it then recommends Shriekback to me. If the system only looked at people *just* like me, it might never find that additional recommendation. Multiply this sytem by hundreds of things I like, cross-referencing with millions of other people’s lists of things they like, and you can see how “explaining” this to users is impossible. So they resort to simplistic things like “People who bought X also bought Y” and other transparent, easy-to-grok systems. But that doesn’t mean that arcane true person-based collab filters can’t also be used on the back end to smartify these engines.

http://www.jivesoftware.com/products/clearspace/

Honestly, I still miss the feature today. It always provided far more interesting choices than their regular product recommendations. Amazon’s main problem there is a lack of real understanding about their products. Recommending a white KitchenAid mixer because I’ve said I own a blue one, or a Nikon D80 with lens because I’ve said I own a Nikon D80 body-only is a waste of everyone’s time.

That would be awesome. Keyword recgonition I guess…

While the itent of the program was to provide a “genuine” peer endorsement experience for a product, it’s pretty clear that it can be misused as someone indicated that the ads looked less like “ads” and more like defametory propaganda, for lack of a better term.

Here’s a link: http://valleywag.com/354279/vc-freaks-out-yahoos-with-shocking-facebook-ad

Your second point (directed @Scott) is also very interesting. When you rate an item, are you rating the item (like a customer review)? Are you rating the quality of the recommendation algorithm? Is my 4 star rating the same as your 4 star rating? Maybe the ratings should be normalized across customers?

I wonder if it matters who people rate items for. If they’re rating it for themselves, the ratings will probably be more accurate than if they’re rating for others…since the social performance might affect how they act. (at least for some people)

You bring up a great point, as well, about trust. You’ve built an app in an environment where trust is crucial, and for patients with diseases they don’t always understand (or are completely new to) it makes all the sense in the world that they’ll try to find someone who has been through the same battles. I like the idea of showing similarity through graphs of treatment data…almost adding to the trust factor.

Thanks for writing, Rich. I’m sure we could all learn a lot from your experience at patientslikeme.

Now you have me wondering…it seems like a difference between Amazon recommendations and Netflix recommendations is that Amazon considers purchase decision to be a positive action, whereas Netflix does not consider renting a positive action.

Netflix, as far as I understand it, explicitly looks at the rating when making recommendations. Does this have the effect of necessitating person-based collaborative filtering, or could you say “people who rated this highly also rated this highly”? Perhaps that’s what they doing anyway?