What should be considered when building a Recommendation Engine?

I've read the book Programming Collective Intelligence and found it fascinating. I'd recently heard about a challenge amazon had posted to the world to come up with a better recommendation engine for their system.

The winner apparently produced the best algorithm by limiting the amount of information that was being fed to it.

As a first rule of thumb I guess... "More information is not necessarily better when it comes to fuzzy algorithms."

I know's it's subjective, but ultimately it's a measurable thing (clicks in response to recommendations).

Since most of us are dealing with the web these days and search can be considered a form of recommendation... I suspect I'm not the only one who'd appreciate other peoples ideas on this.

In a nutshell, "What is the best way to build a recommendation ?"

algorithm recommendation-engine

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Agree with @Ricardo. This question is too broad, like asking "What's the best way to optimize a system?"

One common feature to nearly all existing recommendation engines is that making the final recommendation boils down to multiplying some number of matrices and vectors. For example multiply a matrix containing proximity weights between users by a vector of item ratings.

(Of course you have to be ready for most of your vectors to be super sparse!)

My answer is surely too late for @Allain but for other users finding this question through search -- send me a PM and ask a more specific question and I will be sure to respond.

(I design recommendation engines professionally.)

answered 2011-03-05T05:51:39.297

What should be considered when building a Recommendation Engine?

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