Search ranking estimation
View Patent ↗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.
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.
2. The method of claim 1 , wherein determining the relevance of the documents matching the query comprises determining a probability that a particular document matching the query resides in a particular category.
3. The method of claim 1 , wherein determining the relevance of documents to the query further comprises summing all the relevance values of the particular document over all the categories where the particular document is found.
4. The method of claim 1 , wherein determining the updated, relevance of documents to the query further comprises summing all the updated relevance values of the particular document over all the categories where the particular document is found.
5. The method of claim 1 , wherein each of the weighting factors is less than or equal to one.
6. The method of claim 5 , wherein the sum of all the weighting factors is one.
7. The method of claim 1 , further comprising repeatedly generating the updated probabilities that the categories of the taxonomy contain relevant documents based on the previous updated probabilities that the categories of the taxonomy contain relevant documents and re-determining the updated relevance of documents to the query.
8. The method of claim 7 , wherein generating the updated probabilities that the categories of the taxonomy contain relevant documents ceases when a change in the updated probabilities is less than a convergence threshold.
9. The method of claim 1 , further comprising repeatedly generating the updated probabilities based on the previous updated probabilities and re-determining the updated relevance of documents to the query.
10. The method of claim 9 , wherein generating the updated probabilities ceases when a change in the updated probabilities is less than a convergence threshold.
11. The method of claim 1 , wherein the categories are leaf categories.
12. The method of claim 11 , wherein the leaf categories contain indexed documents related to one another.
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:
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.
14. The apparatus of claim 13 , wherein the SLU is configured to determine the relevance of documents to the query by summing all the relevance values of the particular document over all the categories where the particular document is found.
15. The apparatus of claim 13 , wherein each of the weighting factors is less than or equal to one.
16. The apparatus of claim 15 , wherein the sum of all the weighting factors is one.
17. The apparatus of claim 13 , wherein the SLU is configured to determine the relevance of documents to the query by repeatedly generating the updated probabilities that the categories of the taxonomy contain relevant documents based on the previous updated probabilities that the categories of the taxonomy contain relevant documents and re-determining the updated relevance of documents to the query.
18. The apparatus of claim 17 , wherein the SLU is configured to cease generating the updated probabilities that the categories of the taxonomy contain relevant documents when a change in the updated probabilities is less than a convergence threshold.
19. The apparatus of claim 13 , wherein the SLU is configured to determine the relevance of documents to the query by repeatedly generating the updated probabilities based on the previous updated probabilities and re-determining the updated relevance of documents to the query.
20. The apparatus of claim 19 , wherein the SLU is configured to cease generating the updated probabilities when a change in the updated probabilities is less than a convergence threshold.
21. The apparatus of claim 13 , wherein the categories are leaf categories.
22. The apparatus of claim 21 , wherein the leaf categories contain indexed documents related to one another.