METHODS AND SYSEMS FOR PERFORMING REAL-TIME RECOMMENDATION PROCESSING

• US 20110246465A1
• Filed: 01/10/2011
• Published: 10/06/2011
• Est. Priority Date: 03/31/2010
• Status: Abandoned Application

• Current Assignee: Salesforce.com, Inc.
• Original Assignee: Salesforce.com, Inc.
• Inventors: Gustafson, Erik, Koister, Jari
• Application Number:
  US12/987,932
• Publication Number:
  US 20110246465A1
• Time in Patent Office: Days
• US Class Current: 707/737
• CPC Class Codes: G06F 16/3325 Reformulation based on results of preceding query
• CPC Class Codes: G06F 16/3347 using vector based model
• CPC Class Codes: G06F 16/353 into predefined classes
• CPC Class Codes: G06F 16/355 Class or cluster creation or modification

Abstract

Methods and systems are presented for recommending similar questions to one that a user has entered into a search engine. Previously-entered questions are subject to a clustering algorithm and placed into a hierarchy of clusters, with clusters set within clusters. For each cluster within the hierarchy, a representative vector, based on feature vectors of the items within the cluster, is calculated. A feature vector for the user'"'"'s question is calculated and used, along with the representative vectors at each level in the hierarchy, to traverse and navigate the cluster hierarchy. When a leaf cluster is found, the items in the leaf cluster, such as the previously-entered questions are returned to the user. A subset of items in the leaf cluster, or items from other leaf clusters within a branch cluster, can be selected based on the number of items desired to be returned.

Claims

1. A method for performing recommendation processing, the method comprising:

receiving a query from a user;

creating a feature vector for the query, the feature vector based on content of the query;

traversing, using a processor operatively coupled with a memory, a cluster hierarchy, the cluster hierarchy having clusters within clusters, each cluster of the cluster hierarchy having a representative vector, each representative vector based on at least one feature vector of an element within the respective cluster, the traversing comprising;

finding a closest cluster by comparing the feature vector of the query with representative vectors of clusters at a level in the hierarchy; and

comparing, based on the finding, the feature vector of the query with representative vectors of clusters within the closest cluster;

sending one or more elements in the closest cluster to the user.

2. The method of claim 1 wherein the elements in the cluster hierarchy are previously-entered queries.

3. The method of claim 1 wherein the feature vector is based upon a number of keywords in the query.

4. The method of claim 1 further comprising:

adding the query to the closest cluster of the cluster hierarchy.

5. The method of claim 1 wherein each representative vector is selected from the group consisting of a mean vector and a median vector.

6. The method of claim 1 wherein the sending is triggered upon a desired number of elements being within the closest cluster.

7. The method of claim 1 wherein the sending is triggered upon the closest cluster being a leaf node of the cluster hierarchy.

8. The method of claim 1 wherein the operations are performed in the order as shown.

9. The method of claim 1 wherein each operation is performed by the computer processor operatively coupled to the memory.

10. A computer system executing instructions in a computer program, the computer program instructions comprising program code for performing the operations of claim 1.

11. A machine-readable tangible storage medium embodying information indicative of instructions for causing one or more machines to perform the operations of claim 1.

12. A method for performing recommendation processing, the method comprising:

clustering pre-existing entities into a cluster hierarchy, each level of the hierarchy having a representative vector;

receiving a query from a user;

creating a feature vector for the query;

traversing, using a processor operatively coupled with a memory, the cluster hierarchy, comparing the feature vector of the query to at least one representative vector in the cluster hierarchy to find a closest cluster; and

sending one or more pre-existing entities in the closest cluster to the user.

13. The method of claim 12 wherein the entities include queries.

14. The method of claim 12 wherein the entities are selected from the group consisting of web pages, presentations, Microsoft Word documents, Adobe Acrobat Portable Document Format (PDF) documents, instant messages, tweets, and emails.

15. The method of claim 12 wherein the creating and traversing occur in real time.

16. A method of building a cluster hierarchy for recommendation processing, the method comprising:

receiving items to be clustered, each item having a feature vector;

clustering, using a processor operatively coupled with a memory, the items into leaf clusters;

determining a representative vector for each leaf cluster, the representative vector of each leaf cluster based on at least one feature vector of the items within the respective leaf cluster;

clustering the leaf clusters into branch clusters; and

determining a representative vector for each branch cluster, the representative vector of each branch cluster based on at least one representative vector of the leaf clusters within the respective branch cluster.

17. The method of claim 16 wherein each item is an electronic document selected from the group consisting of a web log, presentation, Microsoft Word document, Adobe Acrobat Portable Document Format (PDF) document, instant message, tweet, and email.

18. The method of claim 16 wherein each feature vector is based upon a number of keywords in the item.

19. The method of claim 16 wherein each feature vector is based upon an author of the item.

20. The method of claim 16 wherein each feature vector is based upon an extracted entity of the item.