Ranking online listings

• US 8,732,177 B1
• Filed: 04/26/2010
• Issued: 05/20/2014
• Est. Priority Date: 04/26/2010
• Status: Active Grant

• Current Assignee: Thryv, Inc. (Mudrick Capital Management LP)
• Original Assignee: JP Morgan Chase Bank, N.A. (JP Morgan Chase & Co)
• Inventors: Chinnasamy, Sivakumar, Kung, Tsu-Jung, Jacob, Jeesmon, Nguyen, Than Kim-Thi
• Primary Examiner(s): Cao, Phuong Thao
• Application Number:
  US12/767,057
• Time in Patent Office: 1,485 Days
• Field of Search: 707/749, 707/999.007, 707/751
• US Class Current: 707/749
• CPC Class Codes: G06F 16/9535 Search customisation based on user profiles and personalisation
• CPC Class Codes: G06F 16/9538 Presentation of query results

Abstract

A search query is received. One or more listings is identified responsive to the search query. For each of the one or more of listings, the following are determined: a relevancy score based on one or more parameters in the search query, an expected click through rate, and at least one of a content density boost that is based on one or more fields that are included in or excluded from the listing and a geography type boost that is based on a comparison of one or more geography parameters of the query to one or more geography parameters of the listing. For each of the one or more listings, a performance score is calculated based on the relevancy score, the expected click through rate, and at least one of the content density boost and the geography type boost.

Claims

1. A method, comprising:

receiving, by a computer, a search query;

identifying, by the computer, one or more listings responsive to the search query;

determining, by the computer, for each of the one or more listings, a relevancy score based on one or more parameters in the search query, an expected click through rate, and a content density boost for a listing category that is a ratio of a click-through rate when a particular type of field is included in a listing in the listing category to a click-through rate when the particular type of field is excluded from a listing in the listing category; and

calculating, by the computer, for each of the one or more listings, a performance score based at least in part on the relevancy score, the expected click through rate, and the content density boost.

2. The method of claim 1, further comprising calculating, for each of the one or more listings, a rank score based on the performance score for the listing and a maximum bid amount for the listing.

3. The method of claim 2, further comprising sorting the one or more listings according to the rank score calculated for each of the one or more listings, and providing the one or more sorted listings in response to the search query.

4. The method of claim 1, further comprising applying one or more weighting factors to at least one of the relevancy score, the expected click through rate, and the content density boost, when calculating the performance score.

5. The method of claim 1, wherein the expected click through rate for a listing is normalized according to a position of the listing in a set of listings when the listing was selected.

6. The method of claim 1, further comprising adjusting the content density boost based on at least one of the one or more parameters in the search query.

7. The method of claim 1, further comprising:

determining, for each of the one or more listings, a geography type boost that is based on a comparison of one or more geography parameters of the search query to one or more geography parameters of the listing; and

calculating the performance score based on the geography type boost in addition to the relevancy score, the expected click through rate, and the content density boost.

8. A non-transitory computer-readable medium embodying a set of computer-executable instructions, the instructions comprising instructions for:

receiving a search query;

identifying one or more listings responsive to the search query;

determining, for each of the one or more listings, a relevancy score based on one or more parameters in the search query, an expected click through rate, and a content density boost for a listing category that is a ratio of a click-through rate when a particular type of field is included in a listing in the listing category to a click-through rate when the particular type of field is excluded from a listing in the listing category; and

calculating, for each of the one or more listings, a performance score based at least in part on the relevancy score, the expected click through rate, and the content density boost.

9. The non-transitory computer-readable medium of claim 8, the instructions further comprising instructions for calculating, for each of the one or more listings, a rank score based on the performance score for the listing and a maximum bid amount for the listing.

10. The non-transitory computer-readable medium of claim 9, the instructions further comprising instructions for sorting the one or more listings according to the rank score calculated for each of the one or more listings, and providing the one or more sorted listings in response to the search query.

11. The non-transitory computer-readable medium of claim 8, the instructions further comprising instructions for applying one or more weighting factors to at least one of the relevancy score, the expected click through rate, and the content density boost, when calculating the performance score.

12. The non-transitory computer-readable medium of claim 8, wherein the expected click through rate for a listing is normalized according to a position of the listing in a set of listings when the listing was selected.

13. The non-transitory computer-readable medium of claim 8, the instructions further comprising instructions for adjusting the content density boost based on at least one of the one or more parameters in the search query.

14. The non-transitory computer-readable medium of claim 8, the instructions further comprising instructions for:

determining, for each of the one or more listings, a geography type boost that is based on a comparison of one or more geography parameters of the search query to one or more geography parameters of the listing; and

calculating the performance score based on the geography type boost in addition to the relevancy score, the expected click through rate, and the content density boost.

15. A system, comprising:

a computing device that is configured to;

receive a search query;

identify one or more listings responsive to the search query;

determine, for each of the one or more listings, a relevancy score based on one or more parameters in the search query, an expected click through rate, and a content density boost for a listing category that is a ratio of a click-through rate when a particular type of field is included in a listing in the listing category to a click-through rate when the particular type of field is excluded from a listing in the listing category; and

calculate, for each of the one or more listings, a performance score based at least in part on the relevancy score, the expected click through rate, and the content density boost.

16. The system of claim 15, the computing device further configured to calculate, for each of the one or more listings, a rank score based on the performance score for the listing and a maximum bid amount for the listing.

17. The system of claim 16, the computing device further configured to sort the one or more listings according to the rank score calculated for each of the one or more listings, and providing the one or more sorted listings in response to the search query.

18. The system of claim 15, the computing device further configured to apply one or more weighting factors to at least one of the relevancy score, the expected click through rate, and the content density boost, when calculating the performance score.

19. The system of claim 15, wherein the expected click through rate for a listing is normalized according to a position of the listing in a set of listings when the listing was selected.

20. The system of claim 15, the computing device further configured to adjust the content density boost based on at least one of the one or more parameters in the search query.

21. The system of claim 15, the computing device further configured to:

determine, for each of the one or more listings, a geography type boost that is based on a comparison of one or more geography parameters of the search query to one or more geography parameters of the listing; and

calculate the performance score based on the geography type boost in addition to the relevancy score, the expected click through rate, and the content density boost.