From MIT News:
The recommendation systems at websites such as Amazon and Netflix use a technique called “collaborative filtering.” To determine what products a given customer might like, they look for other customers who have assigned similar ratings to a similar range of products, and extrapolate from there.
The success of this approach depends vitally on the notion of similarity. Most recommendation systems use a measure called cosine similarity, which seems to work well in practice. Last year, at the Conference on Neural Information Processing Systems, MIT researchers used a new theoretical framework to demonstrate why, indeed, cosine similarity yields such good results.
This week, at the same conference, they are reporting that they have used their framework to construct a new recommendation algorithm that should work better than those in use today, particularly when ratings data is “sparse” — that is, when there is little overlap between the products reviewed and the ratings assigned by different customers.
Using their analytic framework, the researchers showed that, in cases of sparse data — which describes the situation of most online retailers — their “neighbor’s-neighbor” algorithm should yield more accurate predictions than any known algorithm.
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