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 the query from the user;
determining a distribution of nodes of a taxonomy that have non zero probabilities of containing documents relevant to the query;
determining a probability that a document matching the query resides in a particular node;
determining a relevance of documents to the query by multiplying a particular node probability from the distribution of nodes by the probability that a document matching the query resides in the particular node;
for each particular node containing a particular document, determining a value of the relevance of the particular document to the query by multiplying a determined relevance of the particular document matching the query by the initial probability that the particular node includes relevant documents;
generating updated probabilities that the distribution of nodes 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;
generating an updated distribution of nodes by weighting each of the relevance of documents to the query;
determining an updated relevance of documents to the query based on the updated distribution of nodes, updated probabilities, and the relevance of documents matching the query; and
summing the updated relevance values to determine the relevance of the documents to the query.
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Accused Products
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.
31 Citations
12 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 the query from the user; determining a distribution of nodes of a taxonomy that have non zero probabilities of containing documents relevant to the query;
determining a probability that a document matching the query resides in a particular node;determining a relevance of documents to the query by multiplying a particular node probability from the distribution of nodes by the probability that a document matching the query resides in the particular node; for each particular node containing a particular document, determining a value of the relevance of the particular document to the query by multiplying a determined relevance of the particular document matching the query by the initial probability that the particular node includes relevant documents; generating updated probabilities that the distribution of nodes 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; generating an updated distribution of nodes by weighting each of the relevance of documents to the query; determining an updated relevance of documents to the query based on the updated distribution of nodes, updated probabilities, and the relevance of 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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Specification