Ranking documents based on large data sets
First Claim
Patent Images
1. A computer implemented method, comprising:
- creating a ranking model that predicts a likelihood that a document will be selected by;
storing information associated with a plurality of prior searches,determining a prior probability of selection based, at least in part, on the information associated with the prior searches, andgenerating the ranking model based, at least in part on the prior probability of selection;
training the ranking model using a data set that includes approximately tens of millions of instances;
identifying documents relating to a search query;
scoring the documents based, at least in part, on the ranking model;
forming search results for the search query from the scored documents; and
outputting the search results.
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Abstract
A system ranks documents based, at least in part, on a ranking model. The ranking model may be generated to predict the likelihood that a document will be selected. The system may receive a search query and identify documents relating to the search query. The system may then rank the documents based, at least in part, on the ranking model and form search results for the search query from the ranked documents.
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Citations
25 Claims
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1. A computer implemented method, comprising:
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creating a ranking model that predicts a likelihood that a document will be selected by; storing information associated with a plurality of prior searches, determining a prior probability of selection based, at least in part, on the information associated with the prior searches, and generating the ranking model based, at least in part on the prior probability of selection; training the ranking model using a data set that includes approximately tens of millions of instances; identifying documents relating to a search query; scoring the documents based, at least in part, on the ranking model; forming search results for the search query from the scored documents; and outputting the search results. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14)
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15. A system implemented within one or more computer devices, comprising:
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means for receiving a search query; means for identifying documents relating to the search query; means for ranking the documents based, at least in part, on a ranking model trained on a large data set that includes approximately millions of features, the means for ranking includes; means for determining a prior probability of selection corresponding to the search query and one of the identified documents, and means for determining a rank for the one document based, at least in part, on the determined prior probability of selection; means for forming search results for the search query from the ranked documents; and means for outputting the search results.
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16. A system, comprising:
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a repository configured to store information corresponding to a plurality of prior searches; and a server configured to; receive a search query from a user, identify documents corresponding to the search query, rank the identified documents based, at least in part, on a ranking model that includes rules that maximize a likelihood of the repository, when ranking the identified documents, the server is configured to; determine a prior probability of selection corresponding to the search query and one of the identified documents, and determine a rank for the one document based, at least in part, on the determined prior probability of selection, and output the ranked documents. - View Dependent Claims (21, 22)
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17. A system, comprising:
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a repository configured to store information corresponding to a plurality of prior searches; and a server configured to; receive a search query from a user, identify documents corresponding to the search query, rank the identified documents based, at least in part, on a ranking model that includes rules that maximize a likelihood of the repository, when ranking the identified documents, the server is configured to; determine a prior probability of selection corresponding to the search query and one of the identified documents, and determine a rank for the one document based, at least in part, on the determined prior probability of selection, and output the ranked documents. - View Dependent Claims (18, 19, 20)
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23. A method, comprising:
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receiving a search query; identifying documents relating to the search query; determining prior probabilities of selecting each of the documents, where the prior probability of selecting one of the documents is determined based, at least in part, on data regarding at least one of a position of the document within search results, a prior score assigned to the document, or a number of documents above the document in the search results that were selected; determining a score for each of the documents based, at least in part, on the prior probability of selecting the document; generating search results for the search query from the scored documents, and outputting the search results.
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24. A method, comprising:
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creating a ranking model that predicts a likelihood that a document will be selected by; storing information associated with a plurality of prior searches, determining a prior probability of selection based, at least in part, on the information associated with the prior searches, and generating the ranking model based, at least in part, on the prior probability of selection; identifying documents relating to a search query; scoring the documents based, at least in part, on the ranking model; forming search results for the search query from the scored documents; and outputting the search results.
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25. A method, comprising:
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receiving a search query; identifying documents relating to the search query; determining a prior probability of selecting one of the documents, the prior probability of selecting the one document is determined based, at least in part, on data regarding at least one of a position of the one document within search results, a prior score assigned to the one document, or a number of documents above the one document in the search results that were selected; determining a score for the one document based, at least in part, on the prior probability of selecting the one document; generating a list of search results that includes the one document based on the determined score; and outputting the list of search results.
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Specification