Scoring Feed Data Quality

• US 20090063294A1
• Filed: 09/05/2007
• Published: 03/05/2009
• Est. Priority Date: 09/05/2007
• Status: Abandoned Application

• Current Assignee: Oath Inc. (Verizon Communications Inc.)
• Original Assignee: Oath Inc. (Verizon Communications Inc.)
• Inventors: Bui, Tran, Hoekstra, Dennis
• Application Number:
  US11/850,483
• Publication Number:
  US 20090063294A1
• Time in Patent Office: Days
• US Class Current: 705/26
• CPC Class Codes: G06Q 30/02 Marketing; Price estimation or determination; Fundraising
• CPC Class Codes: G06Q 30/0203 Market surveys; Market polls
• CPC Class Codes: G06Q 30/0282 Rating or review of business operators or products
• CPC Class Codes: G06Q 30/0603 Catalogue ordering
• CPC Class Codes: G06Q 30/0629 for generating comparisons

Abstract

A method and apparatus for scoring a data feed is provided. A feed scoring system takes as an input a data summary file produced by the data feed processing engine. The data file contains field coverage data, attribute extraction data, and mapping data. The data file is processed by the feed scoring system to create a set of scores. The scores are organized and presented in a hierarchical manner.

Claims

1. A computer implemented method comprising:

generating a set of feed quality scores for a data feed;

wherein each feed quality score in the set of feed quality scores is a measure of quality of a distinct characteristic of a data feed;

wherein the data feed is electronic information about items, wherein the data feed includes, for each item, a value for at least one field; and

outputting the set of feed quality scores.

2. The method of claim 1 wherein the set of feed quality scores includes a mapped product score;

wherein the mapped product score is a percentage of items in the data feed that were mapped to a comparison grid.

3. The method of claim 1 wherein the set of feed quality scores includes an individual attribute score;

wherein the individual attribute score indicates the number of attributes that were successfully extracted from a name field and a description field in the data feed.

4. The method of claim 1 wherein the set of feed quality scores includes an individual field score;

wherein the individual field score is a percentage of items in the data feed that contained a value for a particular field in the data feed.

5. The method of claim 1 wherein:

the set of feed quality scores includes a score hierarchy, the score hierarchy includes;

one or more finer granularity scores; and

one or more coarser granularity scores derived from said finer granularity scores.

6. The method of claim 1 wherein the data feed is a file.

7. The method of claim 1 wherein a set of summary data is generated based in part on each item represented in said data feed, and said set of feed quality scores is generated based on the set of summary data.

8. The method of claim 7 wherein said set of summary data contains field coverage data:

wherein the field coverage data indicates presence of a value in each field for each item in the data feed.

9. The method of claim 7 wherein said set of summary data contains product categorization data:

wherein the product categorization data indicates the category each item in the data feed belongs to.

10. The method of claim 7 wherein said set of summary data contains comparison grid matching data:

wherein the comparison grid matching data indicates which items in the data feed were mapped in a comparison grid.

11. The method of claim 7 where generating a set of feed quality scores comprises the steps of:

placing items contained in said summary data into categories;

computing a set of fine granularity quality scores based on said items in said categories; and

computing a set of coarse granularity feed quality scores based in part on first set of fine granularity feed quality scores.

12. The method of claim 2 wherein the set of feed quality scores includes a category score that indicates the quality of the data feed with respect to the items which belong to one category:

wherein the category score is based, in part on, the mapped product score.

13. The method of claim 1 wherein said set of feed quality scores includes a type score that indicates the quality of the data feed with respect to all items belonging to one type of items:

wherein the type score is based, in part on, one or more category scores.

14. The method of claim 4 wherein said set of feed quality scores contains a final field score that indicates the quality of all fields for all items belonging to one category in the data feed:

wherein the final field score is based, in part on, on the individual field score.

15. The method of claim 3 wherein said set of feed quality scores contains an attribute extraction score that indicates the number of attributes that were extracted from a name field and a description field in the data feed:

wherein the attribute extraction score is based, in part on, the individual attribute score.

16. The method of claim 1 wherein the data feed is provided by a merchant to a comparison shopping engine.

17. The method of claim 1 wherein multiple data feeds are provided by multiple merchants.

18. The method of claim 1 wherein Calculating quality scores based on quality scores for multiple merchants.

19. A computer-readable medium carrying one or more sequences of instructions which, when executed by one or more processors, causes the one or more processors to perform the method recited in claim 1.

20. A computer-readable medium carrying one or more sequences of instructions which, when executed by one or more processors, causes the one or more processors to perform the method recited in claim 2.

21. A computer-readable medium carrying one or more sequences of instructions which, when executed by one or more processors, causes the one or more processors to perform the method recited in claim 3.

22. A computer-readable medium carrying one or more sequences of instructions which, when executed by one or more processors, causes the one or more processors to perform the method recited in claim 4.

23. A computer-readable medium carrying one or more sequences of instructions which, when executed by one or more processors, causes the one or more processors to perform the method recited in claim 5.

24. A computer-readable medium carrying one or more sequences of instructions which, when executed by one or more processors, causes the one or more processors to perform the method recited in claim 6.

25. A computer-readable medium carrying one or more sequences of instructions which, when executed by one or more processors, causes the one or more processors to perform the method recited in claim 7.

26. A computer-readable medium carrying one or more sequences of instructions which, when executed by one or more processors, causes the one or more processors to perform the method recited in claim 8.

27. A computer-readable medium carrying one or more sequences of instructions which, when executed by one or more processors, causes the one or more processors to perform the method recited in claim 9.

28. A computer-readable medium carrying one or more sequences of instructions which, when executed by one or more processors, causes the one or more processors to perform the method recited in claim 10.

29. A computer-readable medium carrying one or more sequences of instructions which, when executed by one or more processors, causes the one or more processors to perform the method recited in claim 11.

30. A computer-readable medium carrying one or more sequences of instructions which, when executed by one or more processors, causes the one or more processors to perform the method recited in claim 12.

31. A computer-readable medium carrying one or more sequences of instructions which, when executed by one or more processors, causes the one or more processors to perform the method recited in claim 13.

32. A computer-readable medium carrying one or more sequences of instructions which, when executed by one or more processors, causes the one or more processors to perform the method recited in claim 14.

33. A computer-readable medium carrying one or more sequences of instructions which, when executed by one or more processors, causes the one or more processors to perform the method recited in claim 15.

34. A computer-readable medium carrying one or more sequences of instructions which, when executed by one or more processors, causes the one or more processors to perform the method recited in claim 16.

35. A computer-readable medium carrying one or more sequences of instructions which, when executed by one or more processors, causes the one or more processors to perform the method recited in claim 17.

36. A computer-readable medium carrying one or more sequences of instructions which, when executed by one or more processors, causes the one or more processors to perform the method recited in claim 18.