Patent Document ID: 8738547
Application ID: 13083391

Base Claim:
1. A method to perform preference learning on a set of documents, the method comprising: receiving raw input features from the set of documents stored on a data storage device; generating polynomial combinations from the raw input features; generating one or more parameters W; applying W to one or more classifiers to generate outputs; determining a loss function and parameter gradients and updating W; determining one or more sparse regularizing terms and updating W; expressing that one document is preferred over another in a search query and retrieving one or more documents responsive to the search query; and summing over all tuples (q,d + ,d − ) in R: W * = argmin W ⁢ 1  R  ⁢ ∑ ( q , d + , d - ) ∈ R ⁢ L W ⁡ ( q , d + , d - ) , where R is a set of tuples, q is a query, d + is a preferred document, d − is an unprefereed document, and L w is a loss for W.

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Claim 8:
8. The method of claim 1 , comprising performing Stochastic Subgradient Descent (SGD) by: randomly selecting a tuple (q,d+,d −)εR; determining a subgradient of L W t−1 (q,d + ,d − ) with respect to W:∇L W t−1 (q,d 30 ,d − ); and updating W t =W t−1 −η t ∇L W t−1 (q,d +,d − ) where ∇ L W ⁡ ( q , d + , d - ) = { - q ⁡ ( d + - d - ) T if ⁢ ⁢ q T ⁢ W ⁡ ( d + - d - ) < 1 0 otherwise . R is a set of tuples, q is a query, d + is a preferred document, d − is an unprefereed document, L w is a loss for W, t is an iteration count, and η t is a learning rate for a t th iteration.