Spiking Neural Networks Amazon

Why netflix's recommendations are more interesting than amazon's
If you've been to the Amazon

I have always found Netflix's recommendations to be more interesting than Amazon's.  When you buy a Bruce Willis movie on Amazon, they often recommend other Bruce Willis movies.  Netflix, on the other hand, is much more likely to suggest movies I wouldn't have thought of or even heard of.  Why is this?  

Generally recommendation systems can be tuned to simply please the user (emphasize overall popular items) or to wow users (emphasize less popular items).   I think the difference is due not to difference in expertise but difference in goals.  Amazon sells products and Netflix rents them (at least in the case of physical DVDs).  Amazon wants an algorithm that simply optimizes sales, and showing a popular item is more likely to lead to a sale.

Netflix has a different challenge.  They have an inventory of DVDs.  If everyone is renting the most popular movies, they have to stock tons of those movies, and the "long tail" movies sit idle in their warehouse.  They want an algorithm that gets people to go down the tail and watch movies that would otherwise go unwatched and therefore keeping more of their inventory in circulation.  Hence the algorithm is tuned to "going out on a limb, " making the recommendations more interesting.

Source: cdixon's posterous

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