SYSTEM AND METHOD FOR RANKING SEARCH ENGINE RESULTS

• US 20170185602A1
• Filed: 12/05/2016
• Published: 06/29/2017
• Est. Priority Date: 12/28/2015
• Status: Active Grant

• Current Assignee: YE Hub Armenia LLC
• Original Assignee: Yandex Europe AG (Yandex N.V.)
• Inventors: GUSEV, Gleb Gennadievich, NIKULIN, Vadim Andreevich, USTINOVSKIY, Yury Mikhailovich
• Granted Patent: US 10,642,905 B2
• Time in Patent Office: Days
• CPC Class Codes: G06F 16/24578 using ranking
• CPC Class Codes: G06F 16/951 Indexing; Web crawling techniques
• CPC Class Codes: G06N 20/00 Machine learning

Abstract

There are discloses methods and systems for generating a search engine results page (SERP). The method is executable at a server executing a search engine, the server being accessible via a communication network by at least one electronic device. The method comprises, as part of generating a search result list, the search result list containing a first search result and a second search result, predicting a first interest parameter for the first search result; predicting a second interest parameter for the second search result; predicting a usefulness parameter for the first search result, the predicting being at least partially based on the first interest parameter and the second interest parameter; adjusting a position of the first search result within the ranked search result list based on the predicted usefulness parameter, the adjusting resulting in the first search result being at an adjusted position within the ranked search result list.

Claims

1. A method of generating a search engine results page (SERP), the method executable at a server executing a search engine, the server being accessible via a communication network by at least one electronic device, the method comprising:

receiving, from the at least one electronic device, a search query;

generating a search result list, the search result list containing a first search result and a second search result, both of which are responsive to the search query,

ranking the search result list such that as a result of the ranking, the first search result and the second search result are at a first SERP position and a second SERP position, respectively, the first SERP position and the second SERP position being adjacent therebetween;

the ranking resulting in a ranked search result list;

predicting a first interest parameter for the first search result, the predicting based on a first machine learning algorithm;

predicting a second interest parameter for the second search result, the predicting based on a second machine learning algorithm, the second machine learning algorithm being different with the first machine learning algorithm;

predicting a usefulness parameter for the first search result, the predicting being based on a third machine learning algorithm, the predicting being at least partially based on the first interest parameter and the second interest parameter;

adjusting the position of the first search result within the ranked search result list based on the predicted usefulness parameter, the adjusting resulting in the first search result being at an adjusted position within the ranked search result list;

generating the SERP including the first search result and the second search result, the first search result being positioned at the adjusted position of the SERP.

2. The method of claim 1, wherein the first search result is a vertical search result.

3. The method of claim 1, wherein the second search result is a web search result.

4. The method of claim 1, wherein the first machine learning algorithm and the second machine learning algorithm have been trained independently therebetween.

5. The method of claim 4, wherein the third machine learning algorithm has been trained using parameters predicted by the first machine learning algorithm and the second machine learning algorithm.

6. The method of claim 5, wherein parameters predicted by the first machine learning algorithm and the second machine learning algorithm include a first factor indicative of a click-through rate of a given search result and a second factor indicative of a click-through rate of another search result following, in a ranked order, the given search result.

7. The method of claim 6, wherein the third machine algorithm is based at least in part on a modified “

win-loss”

algorithm

8. The method of claim 1, wherein the adjusting the position of the first search result further comprises an additional adjusting the position of the second search result in line with the adjusted position of the first search result.

9. The method of claim 1, wherein the search result list further comprises a third search result ranked at a third SERP position, the third SERP position being adjacent to the second SERP position, the method further comprises:

predicting a third interest parameter for the first search result for the second SERP position, the predicting based on the first machine learning algorithm;

predicting a fourth interest parameter for the third search result, the predicting based on the second machine learning algorithm; and

wherein

the predicting the usefulness parameter for the first search result, further comprises predicting a second usefulness parameter for the first search result, the predicting being based on the third machine learning algorithm, the predicting being at least partially based on the third interest parameter and the fourth interest parameter; and

wherein

the adjusting the position of the first search result further comprises selecting one of the first SERP position and the second SERP position for placing the first search result based on comparison of the usefulness parameter and the second usefulness parameter.

10. The method of claim 9, wherein the predicting usefulness parameter and the predicting the second usefulness parameter are executed substantially simultaneously.

11. The method of claim 10, wherein each of the first interest parameter and the second interest parameter is a respective predicted click-through rate.

12. A method of generating a search engine results page (SERP), the method executable at a server executing a search engine, the server being accessible via a communication network by at least one electronic device, the method comprising:

receiving, from the at least one electronic device, a search query;

generating a search result list, the search result list containing a first search result, a second search result and a third search result, the first search result being a vertical search result and the second search result and the third search result being web search results, all of the first, second and third search results being responsive to the search query;

ranking the second search result and the third search result in a ranked order such that the second search result is at a first ranked position and the third search result is in a second ranked position;

the first ranked position and the second ranked position being adjacent therebetween;

for each of the first ranked position and the second ranked position, determining a respective usefulness parameter for the first search result, the determining being executed by;

predicting a first interest parameter for the first search result at a respective one of each of the first ranked position and the second ranked position, the predicting based on a first machine learning algorithm;

predicting a second interest parameter for a following search result, the predicting based on a second machine learning algorithm, the second machine learning algorithm being different from the first machine learning algorithm;

the following search result being a respective one of the second search result and the third search result;

predicting the respective usefulness parameter for the first search result, the predicting being based on a third machine learning algorithm, the predicting being at least partially based on the first interest parameter and the second interest parameter;

selecting a given one of the first ranked position and the second ranked position for placing the first search result, the given one of the first ranked position and the second ranked position being associated with a highest value of the usefulness parameter;

generating the SERP including the first search result being placed at the given one of the first ranked position and the second ranked position.

13. The method of claim 12, wherein the first machine learning algorithm and the second machine learning algorithm have been trained independently therebetween.

14. The method of claim 13, wherein the third machine learning algorithm has been trained using parameters predicted by the first machine learning algorithm and the second machine learning algorithm.

15. The method of claim 14, wherein parameters predicted by the first machine learning algorithm and the second machine learning algorithm include a first factor indicative of a click-through rate of a given search result and a second factor indicative of a click-through rate of another search result following, in a ranked order, the given search result.

16. The method of claim 14, wherein the third machine algorithm is based at least in part on a modified “

win-loss”

algorithm.

17. The method of claim 12, wherein the search result list further comprises a fourth search result, the fourth search result being another vertical search result, and wherein the method further comprises:

for each of the first ranked position and the second ranked position, determining a respective second usefulness parameter for the fourth search result, the determining being executed;

predicting a first interest parameter for the fourth search result at a respective one of each of the first ranked position and the second ranked position, the predicting based on the first machine learning algorithm;

predicting a second interest parameter for a following search result, the predicting based on the second machine learning algorithm, the second machine learning algorithm being different from the first machine learning algorithm;

the following search result being a respective one of the second search result and the third search result;

predicting the respective second usefulness parameter for the fourth search result, the predicting being based on the third machine learning algorithm, the predicting being at least partially based on the first interest parameter and the second interest parameter;

and wherein the selecting the given one of the first ranked position and the second ranked position comprises;

selecting the given one of the first ranked position and the second ranked position for placing the first search result, the given one of the first ranked position and the second ranked position being associated with a highest value of the usefulness parameter;

selecting the other one of the first ranked position and the second ranked position for placing the fourth search result, the other one of the first ranked position and the second ranked position being associated with a highest value of the second usefulness parameter;

and wherein the generating the SERP comprises;

placing the first search result at the given one of the first ranked position and the second ranked position;

placing the fourth search result at the other one of the first ranked position and the second ranked position.

18. A server comprising:

an information storage medium;

a network interface configured for communication over a communication network;

a processor operationally coupled to the information storage medium and the network interface, the processor being configured to;

receive, from the at least one electronic device, a search query;

generate a search result list, the search result list containing a first search result and a second search result, both of which are responsive to the search query,

rank the search result list such that as a result of the ranking, the first search result and the second search result are at a first SERP position and a second SERP position, respectively, the first SERP position and the second SERP position being adjacent therebetween;

the ranking resulting in a ranked search result list;

predict a first interest parameter for the first search result, the predicting based on a first machine learning algorithm;

predict a second interest parameter for the second search result, the predicting based on a second machine learning algorithm, the second machine learning algorithm being different with the first machine learning algorithm;

predict a usefulness parameter for the first search result, the predicting being based on a third machine learning algorithm, the predicting being at least partially based on the first interest parameter and the second interest parameter;

adjust the position of the first search result within the ranked search result list based on the predicted usefulness parameter, the adjusting resulting in the first search result being at an adjusted position within the ranked search result list;

generate SERP including the first search result and the second search result, the first search result being positioned at the adjusted position of the SERP.