| CPC G06Q 50/2057 (2013.01) [G06F 16/285 (2019.01); G06Q 10/063112 (2013.01); G06Q 10/0639 (2013.01); G06Q 10/105 (2013.01)] | 20 Claims |
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1. A method, comprising:
obtaining, by a device, skill data identifying skills of one or more employees;
obtaining, by the device, historical data,
wherein the historical data is associated with at least one of:
historical employee data,
historical skill data,
historical similarity scores, and/or
historical groups of anchor skill data;
obtaining, by the device and based on the historical data, a set of observations;
partitioning, by the device, the set of observations into a training set;
training a similarity model and/or a clustering model with the training set;
processing, by the device, the skill data, with the similarity model, to determine similarity scores between the skills identified by the skill data,
wherein the similarity model includes a cosine similarity on a weighted bipartite graph model;
adding or removing, by the device, one or more skills to or from the skill data for one or more predefined target skill profile categories, based on the similarity scores and to generate modified skill data,
wherein the predefined target skill profile categories are based on an expert-defined hierarchy of target skill profiles and skills for an entity,
wherein the expert-defined hierarchy of target skill profiles and skills for the entity includes:
a first level corresponding to the entity,
a second level corresponding to one or more predefined target skill profile categories, and
a third level corresponding to one or more skills associated with a predefined target skill profile category of the one or more predefined target skill profile categories;
comparing, by the device, the similarity scores and a predefined threshold to determine anchor skill data from the modified skill data and for each of the predefined target skill profile categories,
wherein comparing the similarity scores and the predefined threshold comprises:
initially identifying a set of skills having a total similarity score above the predefined threshold as anchor skills from one or more seeds of the third level,
selectively adding or removing a skill, of the set of skills, based on comparing a weight of the skill, calculated relative to the predefined target skill profile category, to a particular threshold, and
redefining the expert-defined hierarchy based on selecting, adding or removing the skill,
wherein redefining the expert-defined hierarchy comprises performing an iterative process to continue to add new skills, of the set of skills, as seeds to the one or more seeds in the third level;
grouping, by the device, the anchor skill data for corresponding pluralities of the predefined target skill profile categories, based on one or more predefined group categories and to generate groups of the anchor skill data;
processing, by the device, the groups of the anchor skill data and the similarity scores, with the clustering model, to generate clustered anchor skill data for each of the one or more predefined group categories,
wherein the clustering model includes a hierarchical clustering model that recursively merges communities into a single node and executes modularity clustering on condensed graphs using the similarity scores to generate resulting clusters, and
wherein the clustering model defines a resulting cluster, of the resulting clusters, that contain less than a particular quantity of skills as a unique cluster to reduce granularity of the clustered anchor skill data; and
performing, by the device, one or more actions based on the clustered anchor skill data.
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