Suggestions for you: A better, faster recommendation algorithm

The internet is rife with recommendation systems, suggesting movies you should watch or people you should date. These systems are tuned to match people with items, based on the assumption that similar people buy similar things and have similar preferences. In other words, an algorithm predicts which items you will like based only on your, and the item's, previous ratings.

But many existing approaches to making recommendations are simplistic, says physicist and computer scientist Cristopher Moore, a Santa Fe Institute professor. Mathematically, these methods often assume people belong to single groups, and that each one group of people prefers a single group of items. For example, an algorithm might suggest a science fiction movie to someone who had previously enjoyed another different science fiction movie— - even if the movies have nothing else in common.
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