Search ranking estimation
First Claim
1. In a computerized search system in which queries are submitted by users who receive, in response, a list of documents selected from a corpus of documents wherein the list comprises documents deemed responsive to a user'"'"'s query, a method of determining relevance of the documents comprising:
- obtaining, at a server computer comprising a processor, the query from a user;
determining initial probabilities that a plurality of categories of a taxonomy contain documents relevant to the query, at least one of the initial probabilities being non-zero, wherein the initial probabilities are numeric values;
determining a relevance of the documents matching the query in each category having non-zero initial probability; and
determining a relevance of documents to the query based on the initial probabilities of the plurality of categories and the relevance of the documents matching the query, wherein determining the relevance of documents to the query comprises;
for each particular category containing a particular document, determining a value of the relevance of a particular document to the query by multiplying a determined relevance of the particular document matching the query by the initial probability that the particular category includes relevant documents;
generating updated probabilities that the categories of the taxonomy contain relevant documents by multiplying individual ones of the relevance of documents to the query by a weighting factor, wherein the weighting factors used to generate the updated probabilities decay monotonically with the relevance value of the documents to the query;
determining an updated relevance of documents to the query based on the updated probabilities and the relevance of the documents matching the query; and
summing the updated relevance values to determine the relevance of the documents to the query.
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Abstract
A searcher can be configured to improve relevance ranking of search results through iterative weighting of search ranking results. A Search Auto Categorizer (SAC) operates on a base query to return a probabilistic distribution of leaf categories of a taxonomy in which relevant products may reside. A Search Logic Unit (SLU) can compute a relevance of any particular leaf category to the base query. The SLU can then determine an initial relevance of a particular product to the query based on the probabilistic distribution and the relevance of leaf category to query. The SLU weights the relevance of a product to the query to establish an updated probabilistic distribution. The SLU then repeats the relevance and weighting until convergence upon a relevance list.
53 Citations
22 Claims
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1. In a computerized search system in which queries are submitted by users who receive, in response, a list of documents selected from a corpus of documents wherein the list comprises documents deemed responsive to a user'"'"'s query, a method of determining relevance of the documents comprising:
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obtaining, at a server computer comprising a processor, the query from a user; determining initial probabilities that a plurality of categories of a taxonomy contain documents relevant to the query, at least one of the initial probabilities being non-zero, wherein the initial probabilities are numeric values; determining a relevance of the documents matching the query in each category having non-zero initial probability; and determining a relevance of documents to the query based on the initial probabilities of the plurality of categories and the relevance of the documents matching the query, wherein determining the relevance of documents to the query comprises; for each particular category containing a particular document, determining a value of the relevance of a particular document to the query by multiplying a determined relevance of the particular document matching the query by the initial probability that the particular category includes relevant documents; generating updated probabilities that the categories of the taxonomy contain relevant documents by multiplying individual ones of the relevance of documents to the query by a weighting factor, wherein the weighting factors used to generate the updated probabilities decay monotonically with the relevance value of the documents to the query; determining an updated relevance of documents to the query based on the updated probabilities and the relevance of the documents matching the query; and summing the updated relevance values to determine the relevance of the documents to the query. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12)
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13. In a computerized search system in which queries are submitted by users who receive, in response, a list of documents selected from a corpus of documents wherein the list comprises documents deemed responsive to a user'"'"'s query, an apparatus for determining relevance of the documents comprising:
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a search engine configured to obtain the query from a user; a Search Auto Categorizer (SAC) configured to determine initial probabilities that a plurality of categories of a taxonomy contain documents relevant to the query, at least one of the initial probabilities being non-zero, wherein the initial probabilities are numeric values; and a Search Logic Unit (SLU) configured to; determine a relevance of the documents matching the query in each category having non-zero initial probability; and determine a relevance of documents to the query based on the initial probabilities of the plurality of categories and the relevance of the documents matching the query, wherein the SLU is configured to determine the relevance of documents to the query by; for each particular category containing a particular document, determining a value of the relevance of a particular document to the query by multiplying a determined relevance of the particular document matching the query by the initial probability that the particular category includes relevant documents; generating updated probabilities that the plurality of categories of the taxonomy contain relevant documents by multiplying individual ones of the relevance of documents to the query by a weighting factor, wherein the weighting factors used to generate the updated probabilities decay monotonically with the relevance value of the particular document to the query; determining an updated relevance of documents to the query based on the updated probabilities and the relevance of the documents matching the query; and summing the updated relevance values to determine the relevance of the documents to the query. - View Dependent Claims (14, 15, 16, 17, 18, 19, 20, 21, 22)
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Specification