Processing of categorized product information

• US 8,626,798 B2
• Filed: 09/19/2011
• Issued: 01/07/2014
• Est. Priority Date: 09/21/2010
• Status: Active Grant

• Current Assignee: Alibaba Group Holding Ltd.
• Original Assignee: Alibaba Group Holding Ltd.
• Inventors: Gu, Haijie, Su, Ningjun, Dai, Qifeng, Ma, Haiping, Zhang, Jinyin, Chen, Enhong
• Primary Examiner(s): Lee, Wilson
• Assistant Examiner(s): BUI, THUY T
• Application Number:
  US13/200,124
• Publication Number:
  US 20120072462A1
• Time in Patent Office: 841 Days
• Field of Search: 707/797
• US Class Current: 707/797
• CPC Class Codes: G06F 16/2246 Trees, e.g. B+trees
• CPC Class Codes: G06F 16/26 Visual data mining; Browsing structured data
• CPC Class Codes: G06F 16/954 Navigation, e.g. using categorised browsing
• CPC Class Codes: G06Q 10/087 Inventory or stock management, e.g. order filling, procurement or balancing against orders
• CPC Class Codes: G06Q 30/0625 Directed, with specific intent or strategy

Abstract

Data processing includes accessing a product category tree, the product category tree comprising a plurality of hierarchical levels. A leaf category level of the plurality of hierarchical levels comprises a leaf category node. The leaf category node includes product information. The product information comprises a plurality of product attribute parameters. The plurality of product attribute parameters comprises standard product information. Data processing further includes selecting, among the plurality of product attribute parameters that correspond to the leaf category node, a representative product attribute parameter that is representative of the product information; and partitioning standard product information of the leaf category node to obtain a plurality of sets using the representative product attribute parameter, wherein each of the plurality of sets includes at least some of the standard product information.

Claims

1. A data processing system, comprising:

one or more processors configured to;

access a product category tree, the product category tree comprising a plurality of hierarchical levels, wherein;

a leaf category level of the plurality of hierarchical levels comprises a leaf category node;

the leaf category node includes product information;

the product information comprises a plurality of product attribute parameters;

the plurality of product attribute parameters comprises standard product information and non-standard product information; and

the standard product information comprises a plurality of non-product attribute parameters;

select, among the plurality of product attribute parameters that correspond to the leaf category node, a representative product attribute parameter that is representative of the product information;

partition standard product information of the leaf category node to obtain a plurality of sets using the representative product attribute parameter, wherein each of the plurality of sets includes at least some of the standard product information;

determine a degree of similarity of non-product attribute parameters;

partition the standard product information within one of the plurality of sets into a plurality of subsets such that the degree of similarity within each subset meets a threshold value; and

determine whether values of product attribute parameters that correspond to the non-standard product information and used for set partitioning meet an established criterion; and

one or more memories coupled to the one or more processors, configured to provide the one or more processors with instructions.

2. The system of claim 1, wherein the representative product attribute parameter was previously used by users to search for products.

3. The system of claim 1, wherein a set in the plurality of sets includes a product attribute parameter with identical value for all products associated with the set.

4. The system of claim 1, wherein the processor is further configured to use the plurality of sets to make a product recommendation.

5. The system of claim 1, wherein the processor is further configured to search the plurality of sets to provide a search result.

6. The system of claim 1, wherein the processor is further configured to use the plurality of subsets to make a product recommendation.

7. The system of claim 1, wherein the processor is further configured to:

in the event that the values of product attribute parameters that correspond to the non-standard product information and used for set partitioning do not meet the established criterion, determine a subset for which a correlation of the non-product attribute parameters to the non-standard product information meets a third threshold value.

8. The system of claim 7, wherein the processor is further configured to:

in the event that at least some of the values of product attribute parameters that correspond to the non-standard product information and used for set partitioning meet an established criterion, determine a set to which the non-standard product information belongs based on the at least some of the values of product attribute parameters.

9. The system of claim 8, wherein the processor is further configured to partition the non-standard product information into a subset of the set to which the non-standard product information belongs.

10. The system of claim 9, wherein the processor is further configured to deduplicate standard product information and non-standard product information.

11. A data processing method, comprising:

accessing a product category tree, the product category tree comprising a plurality of hierarchical levels, wherein;

a leaf category level of the plurality of hierarchical levels comprises a leaf category node;

the leaf category node includes product information;

the product information comprises a plurality of product attribute parameters;

the plurality of product attribute parameters comprises standard product information and non-standard product information; and

the standard product information comprises a plurality of non-product attribute parameters;

selecting, among the plurality of product attribute parameters that correspond to the leaf category node, a representative product attribute parameter that is representative of the product information;

partitioning standard product information of the leaf category node to obtain a plurality of sets using the representative product attribute parameter, wherein each of the plurality of sets includes at least some of the standard product information;

determining, using one or more computer processors, a degree of similarity of non-product attribute parameters;

partitioning the standard product information within one of the plurality of sets into a plurality of subsets such that the degree of similarity within each subset meets a threshold value; and

determining whether values of product attribute parameters that correspond to the non-standard product information and used for set partitioning meet an established criterion.

13. The method of claim 11, wherein the representative product attribute parameter was previously used by users to search for products.

14. The method of claim 11, wherein a set in the plurality of sets includes a product attribute parameter with identical value for all products associated with the set.

15. The method of claim 11, further comprising using the plurality of sets to make a product recommendation.

16. The method of claim 11, further comprising searching the plurality of sets to provide a search result.

17. The method of claim 11, further comprising using the plurality of subsets to make a product recommendation.

12. A computer program product for data processing, the computer program product being embodied in a non-transitory computer readable storage medium and comprising computer instructions for:

accessing a product category tree, the product category tree comprising a plurality of hierarchical levels, wherein;

a leaf category level of the plurality of hierarchical levels comprises a leaf category node;

the leaf category node includes product information;

the product information comprises a plurality of product attribute parameters; and

the plurality of product attribute parameters comprises standard product information and non-standard product information; and

the standard product information comprises a plurality of non-product attribute parameters;

selecting, among the plurality of product attribute parameters that correspond to the leaf category node, a representative product attribute parameter that is representative of the product information;

partitioning standard product information of the leaf category node to obtain a plurality of sets using the representative product attribute parameter, wherein each of the plurality of sets includes at least some of the standard product information;

determining a degree of similarity of non-product attribute parameters;

partitioning the standard product information within one of the plurality of sets into a plurality of subsets such that the degree of similarity within each subset meets a threshold value; and

determining whether values of product attribute parameters that correspond to the non-standard product information and used for set partitioning meet an established criterion.

18. The computer program product of claim 12, wherein the representative product attribute parameter was previously used by users to search for products.

19. The computer program product of claim 12, wherein a set in the plurality of sets includes a product attribute parameter with identical value for all products associated with the set.

20. The computer program product of claim 12, further comprising computer instructions for using the plurality of sets to make a product recommendation.

21. The computer program product of claim 12, further comprising computer instructions for searching the plurality of sets to provide a search result.

22. The computer program product of claim 12, further comprising computer instructions for using the plurality of subsets to make a product recommendation.