Semantic search using deep networks****

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Hire Ground is a leading service provider of Applicant Tracking Software (ATS) supporting its clients in their human resource hiring and retention efforts. A key component of ATS is the ability to compare candidates regarding their suitability for a given job description, the most time-consuming and labour intensive task of the hiring process because it depends on the number of candidates, which can often be in the hundreds. Matching resumes with job descriptions computationally is a major challenge due to language variety and nuance used in resumes. **We propose to adapt novel semantic methods developed in our lab in the last several years for automatically ranking resumes of applicants to a given job ad on their suitability for the position. The relevance of expertise will be estimated based on the semantic distance between the job description and each resume based on a term embedding representation of resume and job description. We will evaluate different variants of deep networks, such as Convolutional and Recurrent Neural Networks, and different variants of word embeddings. A key component of the proposed methodology is the human-in-the-loop: the human expert (human resources specialist of a company) will provide feedback to allow the training of the deep network models with minimal training data. The research will advance the state of the art in semantic search and help the industry partner achieve a competitive advantage in their market. ****