Abstract: The present disclosure provides a system and method for recommending context and sequence aware based training set to a user. The system identifies various items and keywords of a plurality of earlier trainings of the users' interest and generates a context and sequence aware recommendation model based on the context of the identified keywords. It uses a collapsed Gibbs Sampling as in generative modelling for prior trainings. Further, it applies the context and sequence aware recommendation model on various keywords that are of users' interest. The context and sequence aware recommendation model infers a plurality of subsequent trainings based on context derived from the keywords. In addition to this, the model is generated to rank the inferred plurality of subsequent topics using a probability distribution over subsequent keywords. At the last, it recommends at least one topic to the user based on ranking of the plurality of trainings.

Abstract: Event prediction system and method includes gathering data corresponding to multiple entities to derive multiple entity profiles. Next, a first subset of entity profiles is identified from the multiple entity profiles generated. The identification is done on the basis of characteristics associated with the entities. Subsequent to identification of the first subset of the entity profiles, a second subset of entity profiles is shortlisted. Here, the second subset of entity profiles shows highest probability of occurrence of the event. Further, a determination of a factor that may lead to occurrence of the event is done.

Abstract: To evaluate performance of organizational units using Human Capital Value (HCV), the input data spread across an enterprise may be received from a user. The input data includes employee data, project related data, and organizational unit data for performance evaluation. The input data is analyzed for generating HCV variables. The HCV variables are stored in a repository (108). Further, the HCV variables may be parsed to determine an optimal set of variables. Based on the parsing, an efficiency of each organizational unit is computed. The computing is based on a Multi Criteria Decision Analysis (MCDA) technique. Based on the computing, the organizational units are ranked in a decreasing order of efficiency.

Abstract: A recruiting person handling profiles of candidates need thorough knowledge about various technologies related to requirements posted with respect to a job opening, so as to correctly interpret and identify skill level of each candidate. Lack of knowledge of the recruiting person may result in skilled candidates not getting shortlisted and candidates having no or less relevant skills getting selected, which would affect work force of an organization the candidates are being recruited for. The disclosure herein generally relates to data processing, and, more particularly, to a method and a system for determining skill similarity by using the data processing. The system automatically identifies skills that match each other, and the recruiting person may use this information to identify and shortlist right candidates for the job. The system generates skill vectors for each skill, and by comparing skill vectors of different skills, identifies skills that are similar to each other.

Abstract: A computer-based system and method for intelligent resume search on online repositories is disclosed. The parameters in the resumes and the attributes related to the said parameters are identified and extracted by scanning the resumes sequentially and are stored in an index file. Search queries are constructed based on accepted query parts as input. The index file is indexed to locate the parameters relevant to the search queries. An initial score is assigned to the parameters located which is transformed to new score based on identifying additional domain intelligence in the derived attributes related to the located parameters. Finally, the resumes relevant to the parameters with the transformed score are retrieved and displayed.

Abstract: The present disclosure provides a system and method for recommending context and sequence aware based training set to a user. The system identifies various items and keywords of a plurality of earlier trainings of the users' interest and generates a context and sequence aware recommendation model based on the context of the identified keywords. It uses a collapsed Gibbs Sampling as in generative modelling for prior trainings. Further, it applies the context and sequence aware recommendation model on various keywords that are of users' interest. The context and sequence aware recommendation model infers a plurality of subsequent trainings based on context derived from the keywords. In addition to this, the model is generated to rank the inferred plurality of subsequent topics using a probability distribution over subsequent keywords. At the last, it recommends at least one topic to the user based on ranking of the plurality of trainings.

Abstract: The present subject matter relates to method(s) and system(s) to rank human profiles based on selection criteria personalized to a selector. In an embodiment, the method includes obtaining querying criteria from the selector to query a database comprising a set of human profiles. Further, a subset of human profiles is determined from the set of human profiles based on the querying criteria and a default ranking mechanism. Furthermore, a selection based ranking is obtained for the subset of human profiles. Further, based on the selection based ranking, a ranking function is determined that is indicative of a relative inclination of the selector towards the one or more implicit attributes. Such a determination is by capturing at least one implicit attribute in the ranking function from the selection based ranking. Further, the ranking function is applied to rank a fresh set of human profiles based on the ranking function.

Abstract: Event prediction system and method includes gathering data corresponding to multiple entities to derive multiple entity profiles. Next, a first subset of entity profiles is identified from the multiple entity profiles generated. The identification is done on the basis of characteristics associated with the entities. Subsequent to identification of the first subset of the entity profiles, a second subset of entity profiles is shortlisted. Here, the second subset of entity profiles shows highest probability of occurrence of the event. Further, a determination of a factor that may lead to occurrence of the event is done.

Abstract: To evaluate performance of organizational units using Human Capital Value (HCV), the input data spread across an enterprise may be received from a user. The input data includes employee data, project related data, and organizational unit data for performance evaluation. The input data is analyzed for generating HCV variables. The HCV variables are stored in a repository (108). Further, the HCV variables may be parsed to determine an optimal set of variables. Based on the parsing, an efficiency of each organizational unit is computed. The computing is based on a Multi Criteria Decision Analysis (MCDA) technique. Based on the computing, the organizational units are ranked in a decreasing order of efficiency.

Abstract: This disclosure relates generally to employee assessment, and more particularly to identification of high potential employees. A method and system is provided for identifying employees with high potential. The present method and system for selecting a set of employees for testing and then selecting attributes of the employees using various feature extraction techniques. Thereafter, the method includes combining attributes selected from the feature extraction techniques. After combining the attributes, the method may include utilizing a classification or ranking model to label employees as high and non-high potential employees. Further, the method includes receiving attributes and a reference set of employees from a user. Thereafter, a distance based approach is utilized to identify high potential employees.

Abstract: System(s) and method(s) for generating campus recruitment plan for facilitating selection of a candidate for an organization is disclosed. A ranking score is assigned to each college from a list of colleges. Number of candidates to be selected from each college is determined. A predefined number of panels from a large pool of panels by using demographic details of each employee of the organization are selected. A utility score for each panel of the predefined number of panels by using the demographic details and recruitment capability of each employee is calculated. Based on skill set of each interviewer and each candidate, a panel-candidate matching index is computed. An optimal number of interview panels from the predefined number of panels for interviewing the number of candidates are selected based on the panel-candidate matching index and the utility score to generate the campus recruitment plan by using an optimization technique.

Abstract: The present subject matter relates to method(s) and system(s) to rank human profiles based on selection criteria personalized to a selector. In an embodiment, the method includes obtaining querying criteria from the selector to query a database comprising a set of human profiles. Further, a subset of human profiles is determined from the set of human profiles based on the querying criteria and a default ranking mechanism. Furthermore, a selection based ranking is obtained for the subset of human profiles. Further, based on the selection based ranking, a ranking function is determined that is indicative of a relative inclination of the selector towards the one or more implicit attributes. Such a determination is by capturing at least one implicit attribute in the ranking function from the selection based ranking. Further, the ranking function is applied to rank a fresh set of human profiles based on the ranking function.

Abstract: A computer-based system and method for intelligent resume search on online repositories is disclosed. The parameters in the resumes and the attributes related to the said parameters are identified and extracted by scanning the resumes sequentially and are stored in an index file. Search queries are constructed based on accepted query parts as input. The index file is indexed to locate the parameters relevant to the search queries. An initial score is assigned to the parameters located which is transformed to new score based on identifying additional domain intelligence in the derived attributes related to the located parameters. Finally, the resumes relevant to the parameters with the transformed score are retrieved and displayed.