How do recommendation systems work?

Question

I've always been curious as to how these systems work. For example, how do netflix or Amazon determine what recommendations to make based on past purchases and/or ratings? Are there any algorithms to read up on?

Just so there's no misperceptions here, there's no practical reason for me asking. I'm just asking out of sheer curiosity.

(Also, if there's an existing question on this topic, point me to it. "Recommendations system" is a difficult term to search for.)

Answer 1

This is such a commercially important application that Netflix introduced a $1 million prize for improving their recommendations by 10%.

After a couple of years people are getting close (I think they're up around 9% now) but it's hard for many, many reasons. Probably the biggest factor or the biggest initial improvement in the Netflix Prize was the use of a statistical technique called singular value decomposition.

I highly recommend you read If You Liked This, You’re Sure to Love That for an in-depth discussion of the Netflix Prize in particular and recommendation systems in general.

Basically though the principle of Amazon and so on is the same: they look for patterns. If someone bought the Star Wars Trilogy well there's a better than even chance they like Buffy the Vampire Slayer more than the average customer (purely made up example).

Answer 2

The O'Reilly book "Programming Collective Intelligence" has a nice chapter showing how it works. Very readable.

The code examples are all written in Python, but that's not a big problem.

Answer 3

One technique is to group users into clusters and recommend products from other users in the same cluster.