Dynamic metadata filtering for classifier prediction
First Claim
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1. A method to be executed at least in part in a computing device for predicting query results in a document search system utilizing metadata properties, the method comprising:
- receiving a user-specified metadata schema, the user-specified metadata schema comprising metadata stored according to a hierarchical structure;
generating the metadata property table based on the received schema;
receiving the metadata property table;
receiving a classifier model table, wherein the classifier model table comprises a sparse matrix of search terms, wherein the sparse matrix consumes significant system resources when processed;
joining the metadata property table and the classifier model table, wherein prior to joining the metadata property table and the classifier model table, the sparse matrix of search terms is condensed;
applying a filter condition to the joined table based on user-specified query conditions;
obtaining a list of documents satisfying the filter condition;
computing, using a sub-model, a probability of a document satisfying a user-specified query based on the list of documents, wherein computing, using the sub-model, comprises computing, for at least a first and second document in the list of documents, that a first probability of a first document is greater than a first probability of a second document if and only if a second probability of the first document is greater than a second probability of the second document, wherein the sub-model comprises the at least one filter condition applied to the joined metadata property table and the classifier model table; and
presenting the probability to the user.
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Abstract
A classifier is used to predict relevant results with arbitrary filtering conditions specified by the user. The classifier model is stored as a database table and joined with a metadata properties table instead of calculating the query result probability using the full classifier model. A user-specified query based filter is applied to the joined tables to obtain the list of documents satisfying the filter. The probability is then computed using the sub-model.
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Citations
18 Claims
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1. A method to be executed at least in part in a computing device for predicting query results in a document search system utilizing metadata properties, the method comprising:
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receiving a user-specified metadata schema, the user-specified metadata schema comprising metadata stored according to a hierarchical structure; generating the metadata property table based on the received schema; receiving the metadata property table; receiving a classifier model table, wherein the classifier model table comprises a sparse matrix of search terms, wherein the sparse matrix consumes significant system resources when processed; joining the metadata property table and the classifier model table, wherein prior to joining the metadata property table and the classifier model table, the sparse matrix of search terms is condensed; applying a filter condition to the joined table based on user-specified query conditions; obtaining a list of documents satisfying the filter condition; computing, using a sub-model, a probability of a document satisfying a user-specified query based on the list of documents, wherein computing, using the sub-model, comprises computing, for at least a first and second document in the list of documents, that a first probability of a first document is greater than a first probability of a second document if and only if a second probability of the first document is greater than a second probability of the second document, wherein the sub-model comprises the at least one filter condition applied to the joined metadata property table and the classifier model table; and presenting the probability to the user. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8)
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9. A system for predicting query results in a document search system utilizing metadata properties, the system comprising:
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a memory; a processor coupled to the memory, capable of executing; a service application configured to; receive a user defined metadata schema, the user defined metadata schema comprising metadata stored according to a hierarchical structure; generate a metadata property table based on the received schema; a prediction engine configured to; receive a classifier model table based on training data associated with documents stored by the document search system, wherein the classifier model table comprises a sparse matrix of search terms, wherein the sparse matrix consumes significant system resources when processed; join the metadata property table and the classifier model table, wherein prior to joining the metadata property table and the classifier model table, the sparse matrix of search terms is condensed; apply a filter condition to the joined table based on user-specified query conditions; obtain a list of documents satisfying the filter condition; and compute, using a sub-model, a probability of a document satisfying a user-specified query based on the list of documents, wherein computing, using the sub-model, comprises computing, for at least a first and second document in the list of documents, that a first probability of a first document is greater than a first probability of a second document if and only if a second probability of the first document is greater than a second probability of the second document, wherein the sub-model comprises the at least one filter condition applied to the joined metadata property table and the classifier model table. - View Dependent Claims (10, 11, 12, 13)
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14. One or more computer-readable storage media, wherein the one or more computer-readable storage media do not consist of a propagated data signal, the one or more computer-readable storage media having stored thereon computer executable instructions that, when executed by a processor, predict query results in a document search system utilizing metadata properties, the instructions comprising:
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receiving custom metadata schema, the custom metadata schema comprising metadata stored according to a hierarchical structure; generating a metadata property table based on the received schema; receiving a classifier model matrix based on a training data set associated with documents processed by the system, wherein the classifier model matrix comprises a sparse matrix of search terms, wherein the sparse matrix consumes significant system resources when processed; generating a classifier model table from the sparse classifier model matrix; joining the metadata property table and the classifier model table, wherein prior to joining the metadata property table and the classifier model table, the sparse matrix of search terms is condensed; applying at least one filter condition to the joined table based on user-specified query conditions; obtaining a list of documents satisfying the at least one filter condition; and computing, using a sub-model, a probability of a document satisfying a user-specified query based on the list of documents, wherein computing, using the sub-model, comprises computing, for at least a first and second document in the list of documents, that a first probability of a first document is greater than a first probability of a second document if and only if a second probability of the first document is greater than a second probability of the second document, wherein the sub-model comprises the at least one filter condition applied to the joined metadata property table and the classifier model table. - View Dependent Claims (15, 16, 17, 18)
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Specification