Many personalized recommendation engines boil down to “here are some items that you might like”. In other words here’s a list, take it or leave it. The output tends to be rather blunt in nature. Recommendations can be served more delicately (depending on how it’s presented), but often a recommender model runs no deeper than an unconnected string of potentially interesting objects.

The formula: Gather user browsing and/or item data, learn from it, and make some item suggestions. Creating this sort of output is more or less what we are taught when building a basic recommendation engine. Be it a…

Graydon Snider

Former atmospheric scientist, now a data scientist at SSENSE

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