Claims:1. A method for providing learning recommendation to an employee of an organization, said method comprising processor implemented steps of:
identifying a plurality of attributes and corresponding attribute scores describing the employee’s profile using an attribute identification module (202);
comparing said identified attribute scores of the corresponding plurality of attributes of the employee with the identified attribute scores of the corresponding attributes with other employees of said organization using a similarity comparison module (204);
identifying at least one employee out of the said other employees of the organization having maximum similarity score of said identified attribute scores of the corresponding plurality of attributes of the at least one employee out of the said other employees of the organization with said identified attribute scores of the corresponding plurality of attributes of the employee using a maximum similarity score identification module (206); and
suggesting learning recommendation to the employee based on the identified plurality of attributes of said employee and learning pattern followed by said at least one employee out of the said other employees of the organization using a training recommendation module (208).

2. The method as claimed in claim 1, wherein said plurality of attributes describing an employee’s profile are selected from a group comprising of demographic attributes, role-based attributes project-based attributes and previous training data.

3. The method as claimed in claim 2, wherein the demographic attributes are selected from a group comprising of experience, current role of employee, current grade of employee, current project and project allocation role.

4. The method as claimed in claim 2, wherein the role-based attributes are selected from a group comprising of role name, role class, role duration, role specific skills, role related competency and performance details on each role.

5. The method as claimed in claim 2, wherein the project-based attributes are selected from a group comprising of technology, domain, nature of technology, complexity of project, type of project, project size, roles present in project, tasks, deliverables, phase wise contribution level, percentage of contribution in each task, duration of phases, percentage of contribution of an employee in each phase, application area, software used, tools and frameworks used and customer.

6. The method as claimed 5, wherein nature of technology is selected from a group comprising of generic and niche.

7. The method as claimed 5, wherein project size is selected from a group comprising of team size, total effort, budget and duration.

8. The method ad claimed in claim 1, wherein said identified attribute scores of the corresponding attributes of the employee are based on specific weightages given to the corresponding attributes.

9. The method as claimed in claim 1, wherein identification at least one employee out of the said other employees of the organization having maximum similarity score of said identified attribute scores of the corresponding plurality of attributes of the at least one employee out of the said other employees of the organization with said identified attribute scores of the corresponding plurality of attributes of the employee is performed using K nearest neighbor algorithm.

10. The method as claimed in claim 1, wherein the suggesting learning recommendation to the employee is performed by merging the suggestion from said previous training data and learning pattern followed by said at least one employee out of the said other employees of the organization.

11. The method as claimed in claim 1, wherein suggesting learning recommendation to the employee based on the identified plurality of attributes is performed by looking at next training of the employee, by skipping one training of the employee, by looking back one training of the employee, by considering frequency of trainings taken by the employee, by grouping training taken by the employee with technology, by adding project context to trainings taken by the employee and by recording training sequences that occur only after occurrence of some specific training.

12. The method as claimed in claim 11, wherein suggesting learning recommendation to the employee by looking at next training of the employee is performed by looking at next consecutive training and recording the sequences and counting frequency over training dataset.

13. The method as claimed in claim 11, wherein suggesting learning recommendation to the employee by skipping one training of the employee is performed by recording sequence by skipping next training and recording sequences by considering next to next training.

14. The method as claimed in claim 11, wherein suggesting learning recommendation to the employee by looking back one training of the employee is performed by looking back one training and recording the sequence.

15. The method as claimed in claim 11, wherein suggesting learning recommendation to the employee by considering frequency of trainings taken by the employee is performed by considering trainings that occur together and recording all possible combinations.

16. The method as claimed in claim 11, wherein suggesting learning recommendation to the employee by grouping training taken by the employee with technology is performed by linking trainings taken with their technology group.

17. The method as claimed in claim 11, wherein suggesting learning recommendation to the employee by adding project context to trainings taken by the employee is performed by avoiding trainings taken during project transition sequences.

18. The method as claimed in claim 11, wherein suggesting learning recommendation to the employee by recording training sequences that occur only after occurrence of some specific training is performed by recording training sequences wherein a certain training occurs only after the occurrence of another training.

19. A system for providing learning recommendation to an employee of an organization, said system comprising:
a processor;
a data bus coupled to said processor;
a computer-usable medium embodying computer code, said computer-usable medium being coupled to said data bus, said computer program code comprising instructions executable by said processor and configured for operating;
an attribute identification module (202) adapted for identifying a plurality of attributes and corresponding attribute scores describing the employee’s profile;
a similarity comparison module (204) adapted for comparing said identified attribute scores of the corresponding plurality of attributes of the employee with the identified attribute scores of the corresponding attributes with other employees of said organization;
a maximum similarity score identification module (206) adapted for identifying at least one employee out of the said other employees of the organization having maximum similarity score of said identified attribute scores of the corresponding plurality of attributes of the at least one employee out of the said other employees of the organization with said identified attribute scores of the corresponding plurality of attributes of the employee; and
a training recommendation module (208) adapted for suggesting learning recommendation to the employee based on the identified plurality of attributes of said employee and learning pattern followed by said at least one employee out of the said other employees of the organization.

, Description:As Attached