Determining relevancy and desirability of terms

• US 7,814,112 B2
• Filed: 02/28/2007
• Issued: 10/12/2010
• Est. Priority Date: 06/09/2006
• Status: Active Grant

• Current Assignee: eBay Inc.
• Original Assignee: eBay Inc.
• Inventors: Gupta, Raghav, Xu, Sichun
• Primary Examiner(s): Vo; Tim T.
• Assistant Examiner(s): Fan; Shiow-Jy
• Application Number:
  US11/679,973
• Publication Number:
  US 20070288433A1
• Time in Patent Office: 1,322 Days
• Field of Search: 707 1- 7, 707/E17.071, 707/999.003, 707/758, 707/748, 707/752
• US Class Current: 707/758
• CPC Class Codes: G06F 16/24578 using ranking
• CPC Class Codes: G06F 16/3334 Selection or weighting of terms from queries, including natural language queries

Abstract

A system and method to sort search results based upon a desirability value is illustrated. This desirability value may be based upon the difference between a demand value and a supply value. Demand may be based upon user activity such as click-throughs, purchases, price, or location. Supply may be based upon a supply of keywords that may be the number of times a word is used in search or item title. The system and method may include receiving a search query, associating a first numerical value with a keyword that is a part of the search query, tracking user activity associated with the keyword, associating a second numerical value with the keyword based upon the user activity, finding a difference value between the first and second numerical values, associating this difference value with the keyword, sorting keywords based upon the difference values, and returning the search results of the sorting.

Claims

1. A system comprising:

a hardware-implemented receiver to receive a search query;

a first hardware-implemented associator to associate a first numerical value with a first keyword that is a part of the search query, the first numerical value representing a percentage of times the first keyword is referenced in a plurality of search queries;

a hardware-implemented tracker to track user activity associated with the first keyword;

a second hardware-implemented associator to associate a second numerical value with the first keyword based upon the user activity, the second numerical value representing a percentage of times user activity is associated with the first keyword relative to a plurality of user activities;

a hardware-implemented calculator to determine a difference value between the first and second numerical values, and to associate the difference value with the first keyword;

a hardware-implemented sorter to sort a plurality of keywords based upon respective difference values associated with each keyword of the plurality of keywords; and

a hardware-implemented outputer to output a result of the sort.

2. The system of claim 1, wherein the hardware-implemented receiver includes a Graphical User Interface (GUI).

3. The system of claim 1, further comprising:

a second hardware-implemented receiver to receive a search query;

a hardware-implemented extractor to extract keywords from the search query;

a hardware-implemented adder to add the extracted keywords to existing keywords;

a hardware-implemented re-calculator to re-calculate a percentage value relating to each keyword, the percentage value representing a percentage of search queries that each of the existing keywords has been used in a plurality of searches; and

a hardware-implemented storage operatively to store the re-calculated percentage value into a keyword database.

4. The system of claim 3, wherein the hardware-implemented re-calculator is to re-calculate the percentage value based upon each keyword and a probability of an appearance in a search query result by the keyword.

5. The system of claim 1, further comprising:

a second hardware-implemented receiver to receive activity data;

a hardware-implemented extractor to extract a second keyword from the activity data to create an activity data set;

a hardware-implemented calculator to calculate a percentage the second keyword is used in the activity data set;

a hardware-implemented averager to computer a new average based upon averaging in the percentage to an existing user activity percentage stored in a demand database, the activity percentage reflecting user activity relating to the second keyword relative to activity relating to a plurality of keywords; and

a hardware-implemented storage operatively coupled to the server to store the new average value into a keyword database.

6. The systems of claim 1, wherein user activity is selected from the group consisting of clicks, user bids, purchases, and added to watch list activities.

7. The system of claim 1, further comprising the hardware-implemented tracker to track user activity associated with the first keyword, the user activity tracked based upon a predefined criteria including a period of time, and after recording a predefined number of user actions.

8. The system of claim 1, further comprising a hardware-implemented updator to update a database with the difference value.

9. The system of claim 1, further comprising:

a second hardware-implemented receiver to receive a sort request of search results;

a hardware-implemented fetcher to fetch a list of keywords associated with the search results from a database; and

a hardware-implemented adder to add up desirability values associated with each keyword.

10. The system of claim 1, wherein the first numerical value is a demand value.

11. The system of claim 1, wherein the second numerical value is a supply value.

12. The system of claim 1, wherein the difference value is a desirability value.

13. A method comprising:

using a hardware-implemented associator to associate a first numerical value with a first keyword that is a part of a search query, the first numerical value representing a percentage of times the first keyword is referenced in a plurality of search queries;

using a hardware-implemented tracker to track user activity associated with the first keyword;

using a second hardware-implemented associator to associate a second numerical value with the first keyword based upon the user activity, the second numerical value representing a percentage of times user activity is associated with the first keyword relative to a plurality of user activities;

using a hardware-implemented calculator to find a difference value between the first and second numerical values, and to associate this difference value with the first keyword;

using a hardware-implemented sorter to sort keywords based upon the difference values; and

using a hardware-implemented outputer to output results of the sorting.

14. The method of claim 13, including receiving of the search query through a Graphical User Interface (GUI).

15. The method of claim 13, further comprising:

using a hardware-implemented extractor to extract keywords from the search query;

using a hardware-implemented adder to add the extracted keywords to existing keywords;

using a hardware-implemented re-calculator to re-calculate a percentage value relating to each keyword, the percentage value representing a percentage of search queries that each of the existing keywords has been used in a plurality of searches; and

using a hardware-implemented storage to store the re-calculated percentage values into a keyword database.

16. The method of claim 15, further comprising using a hardware-implemented re-calculator to re-calculate the percentage value based upon each keyword and a probability of appearance in a search query result by the keyword.

17. The method of claim 13, further comprising using a tracker to track user activity associated with the first keyword, the user activity tracked based upon a predefined criteria including a period of time, and after recording a predefined number of user actions.

18. The method of claim 13, further comprising:

using a hardware-implemented receiver to receive activity data;

using a hardware-implemented extractor to extract a second keyword from activity data to create an activity data set;

using a hardware-implemented calculator to calculate a percentage the second keyword is used in the activity data set;

using a hardware-implemented average to average in the percentage to an existing user activity percentage stored in a demand database to generate a new average value, the activity percentage reflecting user activity relating to the second keyword relative to activity relating to a plurality of keywords; and

using a hardware-implemented storage to store the new average value into a keyword database.

19. The method of claim 13, wherein user activity is at least one of clicks, user bids, purchases, or added to watch list activities.

20. The method of claim 13, further comprising updating a database with the difference value.

21. The method of claim 13, further comprising:

using a hardware-implemented receiver to receive a sort request of search results;

using a hardware-implemented fetcher to fetch a list of keywords associated with the search results from a database; and

using a hardware-implemented adder to add up difference values associated with each keyword.

22. The method of claim 13, wherein the first numerical value is a demand value.

23. The method of claim 13, wherein the second numerical value is a supply value.

24. The method of claim 13, wherein the difference value is a desirability value.