Claims:WE CLAIM:
1. A method for classification of web browsing history, the method comprising:
receiving, by a classification system (101), the web browsing history from a web browser (115) associated with a user, wherein the web browsing history comprises details about one or more web pages browsed by the user;
extracting, by the classification system (101), one or more keywords from each of the one or more web pages browsed by the user based on a trained keyword dataset;
determining, by the classification system (101), a plurality of classifications for each of the one or more web pages based on the one or more keywords;
generating, by the classification system (101), a relevancy matrix between each of the one or more keywords of the one or more web pages and the corresponding plurality of classifications; and
identifying, by the classification system (101), a classification from the plurality of classifications for each of the one or more webpages based on the relevancy matrix, wherein a snapshot of the classification is stored in a non-volatile storage unit (117) of the web browser (115).

2. The method as claimed in claim 1, further comprising identifying a classification for the one or more web pages browsed by the user based on the snapshot of the classification stored in the non-volatile storage unit of the web browser.

3. The method as claimed in claim 1, wherein the details about the one or more web pages comprises metadata of the web page, data regarding header, Uniform Resource Locator (URL), title and time stamp in the one or more web pages browsed by the user.

4. The method as claimed in claim 1, wherein the web browsing history is received from the web browser at pre-defined time intervals.

5. The method as claimed in claim 1, wherein extracting the one or more keywords comprises:
providing, by the classification system, the details about the one or more web pages to a trained keyword extraction model, wherein the trained keyword extraction model is trained using a plurality of training web page dataset; and
extracting, by the classification system, the one or more keywords for each of the one or more web pages based on analysis of the trained keyword extraction model.

6. The method as claimed in claim 1, wherein the plurality of classifications associated with each of the one or more webpages are determined by identifying common characteristics between the one or more keywords and parameters of each web browsing path using a trained classification dataset.

7. The method as claimed in claim 1, wherein the trained classification dataset comprises one or more classification identified previously based on the web browsing history of the user.

8. The method as claimed in claim 1 further comprising adding a user defined keyword to the trained keyword dataset.

9. The method as claimed in claim 1, wherein the relevancy matrix is generated by calculating a cosine similarity score between the one or more keywords of the one or more web pages and the corresponding plurality of classifications.

10. The method as claimed in claim 1, wherein the snapshot of the classification comprises details about time stamp, URL, title, cosine similarity score and relevancy matrix for each of the one or more web pages.

11. A classification system (101) for classification of web browsing history, comprising:
a processor (113); and
a memory (111) communicatively coupled to the processor (113), wherein the memory (111) stores processor instructions, which, on execution, causes the processor (113) to:
receive the web browsing history from a web browser (115) associated with a user, wherein the web browsing history comprises details about one or more web pages browsed by the user;
extract one or more keywords from each of the one or more web pages browsed by the user based on a trained keyword dataset;
determine a plurality of classifications for each of the one or more web pages based on the one or more keywords;
generate a relevancy matrix between each of the one or more keywords of the one or more web pages and the corresponding plurality of classifications; and
identify a classification from the plurality of classifications for each of the one or more webpages based on the relevancy matrix, wherein a snapshot of the classification is stored in a non-volatile storage unit (117) of the web browser (115).

12. The classification system (101) as claimed in claim 11, wherein the processor identifies a classification for the one or more web pages browsed by the user based on the snapshot of the classification stored in the non-volatile storage unit of the web browser.

13. The classification system (101) as claimed in claim 11, wherein the details about the one or more web pages comprises metadata of the web page, data regarding header, Uniform Resource Locator (URL), title and time stamp in the one or more web pages browsed by the user.

14. The classification system (101) as claimed in claim 11, wherein the processor receives the web browsing history from the web browser at pre-defined time intervals.

15. The classification system (101) as claimed in claim 11, wherein the processor extracts the one or more keywords by:
providing the details about the one or more web pages to a trained keyword extraction model, wherein the trained keyword extraction model is trained using a plurality of training web page dataset; and
extracting the one or more keywords for each of the one or more web pages based on analysis of the trained keyword extraction model.

16. The classification system (101) as claimed in claim 11, wherein the processor determines the plurality of classifications associated with each of the one or more webpages by identifying common characteristics between the one or more keywords and parameters of each web browsing path using a trained classification dataset.

17. The classification system (101) as claimed in claim 11, wherein the trained classification dataset comprises one or more classification identified previously based on the web browsing history of the user.

18. The classification system (101) as claimed in claim 11, wherein the processor adds a user defined keyword to the trained keyword dataset.

19. The classification system (101) as claimed in claim 11, wherein the processor generates the relevancy matrix by calculating a cosine similarity score between the one or more keywords of the one or more web pages and the corresponding plurality of classifications.

20. The classification system (101) as claimed in claim 11, wherein the snapshot of the classification comprises details about time stamp, URL, title, cosine similarity score and relevancy matrix for each of the one or more web pages.

Dated this 9th day of March, 2017

R Ramya Rao
Of K&S Partners
Agent for the Applicant
, Description:TECHNICAL FIELD
The present subject matter is related in general to classification system, more particularly, but not exclusively, to a method and system for classification of web browsing history.