Recommendation Engines

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I started using Netflix and I’m both impressed with the recommendation engine and curious. Amazon does a great job using a combination of items and behaviour to present thing I might like to purchase. I find the Netflix Canada content recommendations good but not quite as good. This could be the difference that my Amazon history dates back to June 24, 1999 with my @davidcrow.ca email address. I started thinking about how simple these recommendations seem, i.e., basic inputs and outputs but potentially very powerful business implications. You can see recommendations at Amazon, TiVO, Netflix, Pandora, etc. It’s a great tool for discover and can potentially increase sales and business metrics (back in 2006 Amazon was reporting 35% of sales come from recommendations). Recommendations can be incredibly valuable business drivers (see Digg Recommendation Engine to drive traffic) and I was very curious at how others had  approached this problem, specifically if there were methods or techniques that I could leverage. (I’ve been following the creation of a taste graph or interest graphs by companies like Hunch and Quora and Gravity). The other interesting spot where I see a lot of people discovery is on dating sites, however, I think the success metrics of profile matching for certain dating sites is very different (think eHarmony vs Ashley Madison).

There is a lot of really great tools. I built a Hadoop instance and had Mahout up and running on my MacBook Air in about an hour. It’s not as fancy as the backend at Backtype (check out all of their tech). I’m curious at what others like EventBrite are using to power their social discovery of events and Chomp for mobile apps.