Linear combination of rankers
First Claim
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1. An apparatus, comprising:
- a processor; and
a memory that comprises a plurality of components that are executed by the processor, the plurality of components comprising;
a receiver component that receives first scores for training points and second scores for the training points, wherein the first scores are individually assigned to the training points by a first ranker component and the second scores are individually assigned to the training points by a second ranker component; and
a determiner component in communication with the receiver component that automatically outputs a value for a parameter α
based at least in part upon the first scores and the second scores, wherein α
is used to linearly combine the first ranker component and the second ranker component, the determiner component comprising a metric computer component that uses an information retrieval metric method to determine the value of α
, such that the value α
corresponds to an optimal linear combination of the first and second ranker components with respect to the information retrieval metric method, the information retrieval metric method being one of Normalized Discounted Cumulative Gain, Mean Average Precision, Mean Reciprocal Rank, Bpref, Q-measure, or generalized average precision.
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Abstract
Described herein is a system that includes a receiver component that receives first scores for training points and second scores for the training points, wherein the first scores are individually assigned to the training points by a first ranker component and the second scores are individually assigned to the training points by a second ranker component. The apparatus further includes a determiner component in communication with the receiver component that automatically outputs a value for a parameter α based at least in part upon the first scores and the second scores, wherein α is used to linearly combine the first ranker component and the second ranker component.
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Citations
19 Claims
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1. An apparatus, comprising:
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a processor; and a memory that comprises a plurality of components that are executed by the processor, the plurality of components comprising; a receiver component that receives first scores for training points and second scores for the training points, wherein the first scores are individually assigned to the training points by a first ranker component and the second scores are individually assigned to the training points by a second ranker component; and a determiner component in communication with the receiver component that automatically outputs a value for a parameter α
based at least in part upon the first scores and the second scores, wherein α
is used to linearly combine the first ranker component and the second ranker component, the determiner component comprising a metric computer component that uses an information retrieval metric method to determine the value of α
, such that the value α
corresponds to an optimal linear combination of the first and second ranker components with respect to the information retrieval metric method, the information retrieval metric method being one of Normalized Discounted Cumulative Gain, Mean Average Precision, Mean Reciprocal Rank, Bpref, Q-measure, or generalized average precision. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8, 9)
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10. A method for linearly combining ranker components, comprising:
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receiving first scores for a plurality of training points, wherein each training point in the plurality of training points is individually assigned a score by a first ranker component; receiving second scores for the plurality of training points, wherein each training point in the plurality of training points is individually assigned a score by a second ranker component; and linearly combining the first ranker component and the second ranker component to generate a linear combination of the first ranker component and the second ranker component based at least in part upon the first scores and the second scores, wherein the linear combination of the first ranker component and the second ranker component is optimal or substantially optimal with respect to an information retrieval metric method, wherein linearly combining the first ranker component and the second ranker component comprises determining a value of a parameter α
that is used to modify at least one of first scores output by the first ranker component or second scores output by the second ranker component, the value of the parameter α
determined to optimize the linear combination of the first ranker component and the second ranker component with respect to an information retrieval metric, the information retrieval metric being one of Normalized Discounted Cumulative Gain, Mean Average Precision, Mean Reciprocal Rank, Bpref, Q-measure, or generalized average precision. - View Dependent Claims (11, 12, 13, 14, 15)
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16. A search engine that includes a ranker component, the search engine comprising a processor and a memory, the ranker component being a linear combination of at least a first ranker component and a second ranker component, the ranker component created by way of a series of acts, the acts comprising:
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receiving first scores that are individually assigned to training points by the first ranker component; receiving second scores that are individually assigned to the training points by the second ranker component; determining a first value of a parameter α
that causes scores assigned to different training points to be equal, wherein α
is used to linearly combine scores output by the first ranker component with scores output by the second ranker component;assigning a second value to α
that is less than the first value of α
;assigning a third value to α
that is greater than the first value of α
;determining first and second quality metrics for the linear combination of the first ranker component and the second ranker component that correspond to the second value of α and
the third value of α
, respectively;comparing the first and second quality metrics; and selecting either the second value of α
or the third value of α
to use to linearly combine the first ranker component and the second ranker component based at least in part upon the comparison. - View Dependent Claims (17, 18, 19)
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Specification