Enterprise relevancy ranking using a neural network
First Claim
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1. A computer-implemented method of determining a relevancy rank ordering score for a plurality of documents comprising:
- (a) identifying, by at least one processing unit, a finite set of candidate documents;
(b) for each of the candidate documents;
(i) obtaining raw data for a plurality of ranking features associated with the candidate document, the plurality of ranking features comprising at least two of;
BM25, click distance, URL depth, file type, and language of the candidate document;
(ii) transforming the raw data for the plurality of ranking features;
(iii) normalizing the transformed raw data for the plurality of ranking features;
(iv) using a neural network to calculate a relevancy score from the transformed, normalized raw data for the plurality of ranking features, wherein calculating the relevancy score further comprises;
calculating hidden node scores at a plurality of hidden nodes from the transformed, normalized raw data, wherein the transformed, normalized raw data for each of the ranking features is provided to each of the plurality of hidden nodes; and
calculating the relevancy score based on the hidden node scores;
(c) ranking the candidate documents according to the relevancy score for each of the candidate documents; and
(d) displaying a list of the ranked documents.
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Abstract
A neural network is used to process a set of ranking features in order to determine the relevancy ranking for a set of documents or other items. The neural network calculates a predicted relevancy score for each document and the documents can then be ordered by that score. Alternate embodiments apply a set of data transformations to the ranking features before they are input to the neural network. Training can be used to adapt both the neural network and certain of the data transformations to target environments.
252 Citations
17 Claims
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1. A computer-implemented method of determining a relevancy rank ordering score for a plurality of documents comprising:
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(a) identifying, by at least one processing unit, a finite set of candidate documents; (b) for each of the candidate documents; (i) obtaining raw data for a plurality of ranking features associated with the candidate document, the plurality of ranking features comprising at least two of;
BM25, click distance, URL depth, file type, and language of the candidate document;(ii) transforming the raw data for the plurality of ranking features; (iii) normalizing the transformed raw data for the plurality of ranking features; (iv) using a neural network to calculate a relevancy score from the transformed, normalized raw data for the plurality of ranking features, wherein calculating the relevancy score further comprises; calculating hidden node scores at a plurality of hidden nodes from the transformed, normalized raw data, wherein the transformed, normalized raw data for each of the ranking features is provided to each of the plurality of hidden nodes; and calculating the relevancy score based on the hidden node scores; (c) ranking the candidate documents according to the relevancy score for each of the candidate documents; and (d) displaying a list of the ranked documents. - View Dependent Claims (2, 3, 4, 5, 6)
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7. A system for generating a relevancy ranking for documents comprising:
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at least one processing unit; a memory, communicatively coupled to the at least one processing unit, containing instructions that, when executed by the at least one processing unit, comprise; a module which identifies a set of candidate documents and makes available raw data for a plurality of ranking features for each of the candidate documents, the plurality of ranking features comprising at least two of;
BM25, click distance, URL depth, file type, and language of the candidate document; anda ranking module comprising at least one input transformation, at least one input normalization, and a neural network, wherein the ranking module accepts the raw data for the plurality of ranking features for each of the candidate documents individually, applies the at least one input transformation to the raw data for each of the plurality of ranking features, applies the at least one input normalization to the transformed raw data for each of the plurality of ranking features, provides the transformed, normalized raw data for the plurality of ranking features to the neural network which calculates hidden node scores at a plurality of hidden nodes from the transformed, normalized raw data, wherein the transformed, normalized raw data for each of the ranking features is provided to each of the plurality of hidden nodes, and wherein the neural network calculates a relevancy score based on each of the hidden node scores for each of the candidate documents, and wherein the ranking module ranks the candidate documents and provides a list of the candidate documents for display. - View Dependent Claims (8, 9, 10, 11, 12)
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13. A computer implemented method of rank ordering a plurality of documents by relevancy comprising:
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(a) identifying, by at least one processing unit, a finite set of candidate documents; (b) for each of the candidate documents; (i) obtaining raw data for a plurality of ranking features associated with the candidate document, the plurality of ranking features comprising at least two of;
BM25, click distance, URL depth, file type, and language of the candidate documents;(ii) applying a transformation to the raw data for the plurality of ranking features, wherein the transformation comprises a constant which is configurable; (iii) normalizing the transformed raw data for the plurality of ranking features; (iv) using a neural network to calculate a relevancy score from the transformed, normalized raw data for the plurality of ranking features, wherein calculating the relevancy score further comprises; calculating hidden node scores at a plurality of hidden nodes from the transformed, normalized raw data, wherein the transformed, normalized raw data for each of the ranking features is provided to each of the plurality of hidden nodes; and calculating the relevancy score based on each of the hidden node scores; (c) ordering the candidate documents by the calculated relevancy scores; and (d) displaying a list of the ordered candidate documents. - View Dependent Claims (14, 15, 16, 17)
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Specification