Abstract: Method and apparatus are disclosed for classification of job titles via machine learning. An example system includes memory configured to store a convolutional neural network (CNN). The CNN includes a character-title partial-CNN, a word-title partial-CNN, a description CNN, and at least one fully-connected layer. The example system also includes one or more processors configured to apply the character-title partial-CNN to a title to generate a character-level feature, apply the word-title partial-CNN to the title to generate a first word-level feature, and apply the description partial-CNN to a description to generate a second word-level feature. The one or more processors are configured to generate a posting feature by concatenating the character-level feature, the first word-level feature, and the second word-level feature.

Abstract: Automated systems and methods for determining jobs, skills, and training recommendations are disclosed. An example system includes one or more processors of an employment website entity. The one or more processors are configured to extract job information and skill information from resumes in a resume database, generate a job transition graph based on the job information, generate a job-skill graph based on the job information and the skill information, and generate a skill re-occurrence graph based on the skill information. The one or more processors are configured to determine a jobs matrix by minimizing an objective function based on the job transition graph, the job-skill graph, and the skill re-occurrence graph. The one or more processors are configured to retrieve next job recommendations from the jobs matrix based on a the current job position of a candidate and present, via an employment app, the next-job recommendations for the candidate.