COMPUTER APPLICATION QUERY SUGGESTIONS

• US 20180032523A1
• Filed: 07/27/2016
• Published: 02/01/2018
• Est. Priority Date: 07/27/2016
• Status: Active Grant

• Current Assignee: Microsoft Technology Licensing LLC (Microsoft Corporation)
• Original Assignee: Microsoft Technology Licensing LLC (Microsoft Corporation)
• Inventors: Singhal, Aman, De Barros, Marcelo M., Mittal, Manish, Shenoy, Siddharth S.
• Granted Patent: US 10,387,435 B2
• Time in Patent Office: Days
• CPC Class Codes: G06F 16/24578 using ranking
• CPC Class Codes: G06F 16/285 Clustering or classification
• CPC Class Codes: G06F 16/3322 using system suggestions G06F16/3325 takes precedence
• CPC Class Codes: G06F 16/9024 Graphs; Linked lists G06F16/9027 takes precedence
• CPC Class Codes: G06F 16/90328 using search space presentation or visualization, e.g. category or range presentation and selection
• CPC Class Codes: G06F 16/951 Indexing; Web crawling techniques

Abstract

A plurality of category application queries can be processed, including identifying an associated list of results for each of the category application queries, with each list of results listing one or more applications. A cluster of the category application queries that each have a category in common in an application category stamp can be identified. A proximity graph for the category application queries in the cluster can be generated. The proximity graph can indicate similarity scores based at least in part on numbers of applications in common for lists of results associated with the category application queries in pairs of queries in the cluster. Lists each having one or more suggested alternative category application queries for category application queries in the cluster can be generated, with the generating of the lists of one or more suggested alternative category application queries being based at least in part on the similarity scores.

Claims

1. A computer system comprising:

at least one processor; and

memory comprising instructions stored thereon that when executed by at least one processor cause at least one processor to perform acts comprising;

maintaining a query data store, with the data store comprising a plurality of category application queries, a list of responsive applications for each of the category application queries, and a category stamp for each of the category application queries, with each category stamp indicating a category of applications;

generating a plurality of clusters of the category application queries, with the generating comprising, for each cluster, identifying a plurality of the category application queries with matching category stamps that all indicate a category for the cluster;

for each of the clusters, generating a proximity graph for the category application queries in the cluster, with each proximity graph for a cluster comprising similarity scores for pairs of the category application queries in the cluster, and with each of the similarity scores for a pair of queries being based at least in part on numbers of applications in common between the lists of responsive applications for the pair of queries; and

for each of a plurality of the queries, generating a list of one or more suggested queries from the data store based at least in part on the similarity scores for pairs of queries that include that query.

2. The computer system of claim 1, wherein the category for at least one of the clusters comprises a combination of a plurality of sub-categories for the cluster.

3. The computer system of claim 2, wherein the sub-categories comprise top level application categories from an online application store.

4. The computer system of claim 2, wherein the category for the at least one of the clusters comprises a major sub-category and a minor sub-category, and wherein generating the proximity graph for the at least one of the clusters comprises giving greater weight to common applications in the major sub-category than to common applications in the minor sub-category.

5. The computer system of claim 1, wherein the generating of the lists of one or more suggested queries comprises ranking multiple suggested queries in a list of suggested queries.

6. The computer system of claim 5, wherein the ranking of the suggested queries is based at least in part on the similarity scores.

7. The computer system of claim 1, wherein the acts further comprise:

receiving a live category application query;

matching the live query to a matching query from the data store, with the matching query having a corresponding list of one or more suggested queries of the generated lists of one or more suggested queries; and

in response to the receiving of the live category application query and the matching of the live query to the matching query, returning the list of one or more suggested queries for the matching query.

8. A computer-implemented method, comprising the following acts:

processing a plurality of category application queries, with the processing comprising identifying an associated list of results for each of the category application queries, with each list of results listing one or more applications;

identifying a cluster of the category application queries that each have a category in common in an application category stamp;

generating a proximity graph for the category application queries in the cluster, with the proximity graph indicating similarity scores based at least in part on numbers of applications in common for lists of results associated with the category application queries in pairs of queries in the cluster; and

generating a list of one or more suggested alternative category application queries for each of multiple category application queries in the cluster, with the generating of the lists of one or more suggested alternative category application queries being based at least in part on the similarity scores.

9. The method of claim 8, wherein the category in common for the cluster comprises a combination of a plurality of sub-categories for the cluster.

10. The method of claim 9, wherein the sub-categories comprise top level application categories from an online application store.

11. The method of claim 9, wherein the category in common for the cluster comprises a major sub-category and a minor sub-category, and wherein the generating of the lists of one or more suggested alternative category application queries is based upon giving greater weight to common applications in the major sub-category than to common applications in the minor sub-category.

12. The method of claim 8, wherein the generating of the lists of one or more suggested queries comprises ranking suggested queries in a list of multiple suggested queries.

13. The method of claim 12, wherein the ranking of the suggested queries is based at least in part on the similarity scores.

14. The method of claim 8, wherein the acts further comprise:

receiving a live category application query;

matching the live query to a matching query of the category application queries in the cluster, with the matching query having a corresponding list of one or more suggested alternative category application queries of the generated lists of one or more suggested queries; and

in response to the receiving of the live category application query and the matching of the live query to the matching query, returning the list of one or more suggested alternative category application queries for the matching query.

15. One or more computer-readable memory having computer-executable instructions embodied thereon that, when executed by at least one processor, cause at least one processor to perform acts comprising:

processing a plurality of category application queries, with the processing comprising identifying an associated list of results for each of the category application queries, with each list of results listing one or more applications;

identifying a cluster of the category application queries that each have a category in common in category stamps for the category application queries;

generating a proximity graph for the category application queries in the cluster, with the proximity graph indicating similarity scores based at least in part on numbers of applications in common for lists of results associated with the category application queries in pairs of queries in the cluster; and

generating a list of one or more suggested alternative category application queries for each of multiple category application queries in the cluster, with the generating of the lists of one or more suggested alternative category application queries being based at least in part on the similarity scores;

receiving a live category application query;

matching the live query to a matching query of the category application queries in the cluster, with the matching query having a corresponding list of one or more suggested alternative category application queries of the generated lists of one or more suggested queries; and

in response to the receiving of the live category application query and the matching of the live query to the matching query, returning the list of one or more suggested alternative category application queries for the matching query.

16. The one or more computer-readable memory of claim 15, wherein the category in common for the cluster comprises a combination of a plurality of sub-categories for the cluster.

17. The one or more computer-readable memory of claim 16, wherein the sub-categories comprise top level application categories from an online application store.

18. The one or more computer-readable memory of claim 16, wherein the category in common for the cluster comprises a major sub-category and a minor sub-category, and wherein generating the proximity graph for the cluster comprises giving greater weight to common applications in the major sub-category than to common applications in the minor sub-category.

19. The one or more computer-readable memory of claim 15, wherein the generating the lists of one or more suggested queries comprises ranking suggested queries in a list of multiple suggested queries.

20. The one or more computer-readable memory of claim 19, and wherein the ranking of the suggested queries is based at least in part on the similarity scores.