Claims

1. A method for generating a search ranking score for a web page, the method comprising:receiving training data at a training data processor, the training data including at least a first page, a first label, a second page and a second label;receiving the first page at a feature extraction processor;identifying first features in the first page at the feature extraction processor;calculating first values relating to the first features at the feature extraction processor;receiving a second page at a feature extraction processor;identifying second features in the first page at the feature extraction processor;calculating second values relating to the second features at the feature extraction processor;receiving the first features, the first values, the first label, the second features, the second values, and the second label and at a machine learning server;generating, at the machine learning server, a ranking function for a search engine based on the first features, the first values, the first label, the second features, the second values, and the second label;receiving a web page at a receiving processor;receiving a keyword at the receiving processor; andapplying the ranking function to the web page and keyword to generate a score.

2. The method as recited in claim 1, wherein the score relates to internal links, external links, in-links, or out-links.

3. The method as recited in claim 1, wherein the score relates to a quality of the web page.

4. The method as recited in claim 1, wherein the score relates to a crawlability of the web page.

5. The method as recited in claim 1, wherein the applying the ranking function to the web page includes analyzing features of the web page.

6. The method as recited in claim 5, further comprising:generating a maximum score for the web page based on the ranking function; andgenerating a recommendation for a change to at least one feature of the web page based on the maximum score.

7. The method as recited in claim 6, further comprising generating a prediction of a new score for the web page based on the recommendation.

8. The method as recited in claim 7, further comprising generating an approximation of a change in traffic to the web page based on the recommendation.

9. The method as recited in claim 1, further comprising: receiving a web site including a plurality of web pages;applying the ranking function to each of the plurality of web pages to generate a score for each of the plurality of web pages;calculating a popularity of each of the plurality of web pages;applying a weight to each of the plurality of web pages based on the respective popularity; andgenerating a score for the web site based on the score and weight for each of the plurality of web pages.

10. The method as recited in claim 9, wherein the popularity is calculated using at least one of the GOOGLE page rank algorithm, the ALEXA rank, depth of the web page from a home page and number of visits to the web page.

11. A system for generating a score for a web page, the system comprisinga training data processor, the training data processor effective to receive training data, the training data including at least a first page, a first label, a second page and a second label;a feature extraction processor connected to the training data processor, the feature extraction processor effective to receive the first page, identify first features in the first page and calculate first values relating to the first features; the feature extraction processor further effective to receive the second page and identify second features and calculate second values relating to the second features;a machine learning processor connected to the feature extraction processor, the machine learning processor effective to receive the first features, the first values, the first label, the second features, the second values, and the second label and generate a ranking function for a search engine based on the first features, the first values, the first label, the second features, the second values, and the second label;a receiving processor connected to the machine learning processor, the receiving processor effective to receive a web page and a keyword; anda ranking processor connected to the receiving processor, the ranking processor effective to apply the ranking function to the web page and keyword to generate a score.

12. The system as recited in claim 11, wherein the score relates to internal links, external links, in-links, or out-links.

13. The system as recited in claim 11, wherein the score relates to a quality of the web page.

14. The system as recited in claim 11, wherein the score relates to a crawlability of the web page.

15. The system as recited in claim 11, wherein the ranking processor applies the ranking function to the web page by analyzing features of the web page.

16. The system as recited in claim 15, wherein the ranking processor is further effective to:generate a maximum score for the web page based on the ranking function; andgenerate a recommendation for a change to at least one feature of the web page based on the maximum score.

17. The system as recited in claim 16, wherein the ranking processor is further effective to generate a prediction of a new score for the web page based on the recommendation.

18. The system as recited in claim 17, wherein the ranking processor is further effective to generate an approximation of a change in traffic to the web page based on the recommendation.

19. The system as recited in claim 11, wherein:the receiving processor is effective to receive a web site including a plurality of web pages; andthe ranking processor is effective to:apply the ranking function to each of the plurality of web pages to generate a score for each of the plurality of web pages;calculate a popularity of each of the plurality of web pages;apply a weight to each of the plurality of web pages based on the respective popularity; andgenerate a score for the web site based on the score and weight for each of the plurality of web pages.

20. The system as recited in claim 19, wherein the popularity is calculated using at least one of the GOOGLE page rank algorithm, the ALEXA rank, depth of the web page from a home page and number of visits to the web page.

21. The system as recited in claim 11, wherein the training data processor, the feature extraction processor, the machine learning processor, the receiving processor and the ranking processor are distinct.