PROCESSING MAXIMUM LIKELIHOOD FOR LISTWISE RANKINGS
First Claim
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1. A method for tuning a ranking model used in conjunction with a page search system, the system comprising:
- obtaining a data set, wherein the data set includes queries, documents and metadata;
defining an objective function;
calculating the value of the objective function, wherein the value of the objective function is dependent on the data set; and
tuning the parameters of the ranking model associated with the data set for use in conjunction with a page search system, the tuning of the parameters being based on the value of the objective function, wherein the tuned parameters of the ranking model ultimately change the ranking of the documents in the data set such that the ranking is more consistent with the metadata.
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Abstract
The present invention introduces a new approach to learning systems. More specifically, the present invention provides learned methods for optimize ranking models. In one aspect of the present invention, an objective function is defined as the likelihood of ground truth based on a Luce model. In another aspect, techniques of the present invention provide a way of representing different kinds of ground truths as a constraint set of permutations. In yet another aspect of the present invention, techniques of the present invention provide a way of learning the model parameter by maximizing the likelihood of the ground truth.
22 Citations
20 Claims
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1. A method for tuning a ranking model used in conjunction with a page search system, the system comprising:
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obtaining a data set, wherein the data set includes queries, documents and metadata; defining an objective function; calculating the value of the objective function, wherein the value of the objective function is dependent on the data set; and tuning the parameters of the ranking model associated with the data set for use in conjunction with a page search system, the tuning of the parameters being based on the value of the objective function, wherein the tuned parameters of the ranking model ultimately change the ranking of the documents in the data set such that the ranking is more consistent with the metadata. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8, 9, 13, 14)
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10. A method for preparing data sets for document ranking, wherein the method is configured to utilize different kinds of ground truth as a constraint set of permutations, wherein the method comprises:
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obtaining a first set of ground truth data; obtaining a second set of ground truth data; and combining the first set of ground truth data and the second set of ground truth data into a permutation set, wherein the permutation set is configured and arranged to be processed in a Luce model for ranking. - View Dependent Claims (11, 12)
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15. A method for optimizing a ranking model, wherein the method comprises:
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obtaining a dataset, wherein the dataset contains a plurality of feature dimensions for individual documents; computing a likelihood related to the dataset, wherein the plurality of feature dimensions for individual documents is used to compute the likelihood; computing a gradient with respect to each feature dimension; and
processing modifications to a parameter of the ranking model, wherein the direction of the modification is determined by the direction of the gradient. - View Dependent Claims (16, 17, 18, 19, 20)
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Specification