DETERMINING QUERY INTENT

• US 20110314012A1
• Filed: 06/16/2010
• Published: 12/22/2011
• Est. Priority Date: 06/16/2010
• Status: Active Grant

• Current Assignee: Microsoft Technology Licensing LLC (Microsoft Corporation)
• Original Assignee: Microsoft Corporation
• Inventors: Kenthapadi, Krishnaram N. G., Tsaparas, Panayiotis, Gollapudi, Sreenivas, Agrawal, Rakesh
• Granted Patent: US 8,612,432 B2
• Time in Patent Office: Days
• US Class Current: 707/731
• CPC Class Codes: G06F 16/90335 Query processing

Abstract

A tree structure has a node associated with each category of a hierarchy of item categories. Child nodes of the tree are associated with sub-categories of the categories associated with parent nodes. Training data including received queries and indicators of a selected item category for each received query is combined with the tree structure by associating each query with the node corresponding to the selected category of the query. When a query is received, a classifier is applied to the nodes to generate a probability that the query is intended to match an item of the category associated with the node. The classifier is applied until the probability is below a threshold. One or more categories associated with the nodes that are closest to the intent of the received query are selected and indicators of items of those categories that match the received query are output.

Claims

1. A method comprising:

receiving a plurality of nodes by a computing device, wherein each node is associated with an item category with a plurality of queries and a count for each associated query;

receiving a query by the computing device;

receiving a classifier by the computing device, wherein the classifier, when applied to a node of the plurality of nodes using the received query by the computing device, results in a generated probability that the received query is intended for an item category associated with the node;

applying the classifier to the plurality of nodes using the received query by the computing device until a generated probability for a node is below a threshold probability resulting in a list of item categories and a generated probability for each item category;

ranking the item categories in the list of item categories based on the generated probabilities by the computing device; and

providing the item categories in a ranked order by the computing device through a network.

2. The method of claim 1, further comprising:

determining a highest ranked item category from the ranked item categories;

determining one or more items responsive to the received query that are associated with the highest ranked item category; and

providing indicators of the determined one or more items.

3. The method of claim 1, further comprising:

determining a subset of the ranked item categories;

determining one or more items responsive to the received query that are associated with the item categories of the subset of ranked item categories; and

providing indicators of the determined one or more items.

4. The method of claim 3, further comprising providing the indicators of the determined one or more items grouped by the item categories associated with their indicated items.

5. The method of claim 1, wherein the classifier is a Bayesian classifier.

6. The method of claim 1, wherein the classifier generates a probability that the received query is intended for an item category associated with the node of the plurality of nodes by determining the count associated with the node for the received query and generating the probability using the determined count.

7. The method of claim 1, further comprising setting the threshold probability by the computing device responsive to an input value.

8. A method comprising:

receiving training data comprising a plurality of item categories by a computing device, wherein each item category has one or more associated queries and a count associated with each of the one or more queries;

receiving a plurality of nodes comprising a node for each item category by the computing device, wherein each node in the plurality of nodes is a child of another node, a parent of another node, or both;

for each of the nodes in the plurality of nodes, associating the count for each query associated with the item category of the node with the node by the computing device; and

for each of the nodes in plurality of nodes, associating the count for each query associated with the item category of the node with one or more nodes that are parents of the node by the computing device.

9. The method of claim 8, further comprising:

receiving a threshold probability;

receiving a query;

receiving a classifier, wherein the classifier, when applied to a node of plurality of nodes using the received query, results in a generated probability that the received query is intended for an item category associated with the node;

recursively applying the classifier to the plurality of nodes using the received query until a generated probability for a node is below the received threshold probability resulting in list of item categories and a generated probability for each item category;

ranking the item categories in the list of item categories based on the generated probabilities; and

providing the item categories a in ranked order.

10. The method of claim 9, further comprising:

determining a highest ranked item category;

determining one or more items responsive to the received query that are associated with the highest ranked item category; and

providing indicators of the determined one or more items.

11. The method of claim 9, further comprising:

determining a subset of the ranked item categories;

determining one or more items responsive to the received query that are associated with the item categories of the subset of ranked item categories; and

providing indicators of the determined one or more items.

12. The method of claim 11, further comprising providing the indicators of the determined one or more items grouped by the item categories associated with their indicated items.

13. The method of claim 9, wherein the classifier is a Bayesian classifier.

14. The method of claim 9, wherein the classifier generates a probability that the received query is intended for an item category associated with the node of the plurality of nodes by determining the count associated with the node for the received query and generating the probability using the determined count.

15. A system comprising:

at least one computing device; and

a provider that;

receives a plurality of nodes, wherein each node is associated with an item category and each node is further associated with a plurality of queries and a count for each associated query;

receives a query;

receives a classifier, wherein the classifier, when applied to a node of plurality of nodes using the received query by the provider, results in a generated probability that the received query is intended for an item category associated with the node;

applies the classifier to the plurality of nodes using the received query until a generated probability for a node is below a threshold probability resulting in list of item categories and a generated probability for each item category;

ranks the item categories in the list of item categories based on the generated probabilities; and

provides the item categories in a ranked order.

16. The system of claim 15, wherein the provider further:

determines the highest ranked item category;

determines one or more items responsive to the received query that are associated with the highest ranked item category; and

provides indicators of the determined one or more items.

17. The system of claim 15, wherein the provider further determines the threshold probability.

18. The system of claim 15, wherein the provider further:

determines a subset of the ranked item categories;

determines one or more items responsive to the received query that are associated with the item categories of the subset of ranked item categories; and

provides indicators of the determined one or more items.

19. The system of claim 18, wherein the provider further provides the determined one or more items grouped by the item categories associated with their indicated items.

20. The system of claim 15, wherein the provider further adjusts the threshold probability to increase the precision of the ranked item categories or to provide more specific item categories.