Reference number : GC-2017-Q1-00399, amendment number : , agreement type : G, recipient legal name : Barbosa, Denilson (University of Alberta), recipient country : CA, recipient province : nan, recipient city : Alberta, recipient postal code : nan, program name english : Engage Grants for Universities, program name french : Subventions d'engagement partenarial pour les universités, program purpose english : HireGround is a Canadian company that specializes in tools for matching resumes to job ads, thus relieving the burden on recruiters who could not otherwise process the large volume of applicants for a single job ad. They have clients from different and large segments of the Canadian economy. We seek to enhance multiple aspects of the process, leading to better recruitment decisions and happier employers and employees. Matching and ranking resumes of candidates for job ads is a longstanding challenge in AI. One trend is to use custom-built recommender systems, which work by vectorizing job descriptions and resumes in such a way that qualifications, experience, and certifications correspond to the dimensions of the vectors. Then, matching is done by comparing vectors (of jobs and resumes). As such, recommender systems cannot easily deal with fast-changing sectors such as IT, where new skills appear all the time (requiring all vectors to be recomputed each time)._x000D_
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Information Retrieval (IR) techniques to overcome this problem exist. For example, language modeling can be used to rank resumes (documents) against a single job ad (query), if the resumes and job descriptions have considerable overlapping terminology. In this project we will extend IR-based solutions with a new algorithms for scoring resumes given job descriptions based on word embeddings produced by Deep Learning techniques. Such embeddings are vectorial representations of the words within context. For example, because the words "clustering" and "classification" are used frequently to describe machine learning tasks, they would have similar embeddings. Thus, one can match a resume that mentions one of the terms to a job description mentioning the other, even if approximately. Similarly, merging the word embeddings for the terms "machine" and "learning" will result in a vector that is similar to the embeddings of "clustering" and "classification", allowing for a match where the previous methods would fail._x000D_
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_x000D_, program purpose french : HireGround is a Canadian company that specializes in tools for matching resumes to job ads, thus relieving the burden on recruiters who could not otherwise process the large volume of applicants for a single job ad. They have clients from different and large segments of the Canadian economy. We seek to enhance multiple aspects of the process, leading to better recruitment decisions and happier employers and employees. Matching and ranking resumes of candidates for job ads is a longstanding challenge in AI. One trend is to use custom-built recommender systems, which work by vectorizing job descriptions and resumes in such a way that qualifications, experience, and certifications correspond to the dimensions of the vectors. Then, matching is done by comparing vectors (of jobs and resumes). As such, recommender systems cannot easily deal with fast-changing sectors such as IT, where new skills appear all the time (requiring all vectors to be recomputed each time)._x000D_
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Information Retrieval (IR) techniques to overcome this problem exist. For example, language modeling can be used to rank resumes (documents) against a single job ad (query), if the resumes and job descriptions have considerable overlapping terminology. In this project we will extend IR-based solutions with a new algorithms for scoring resumes given job descriptions based on word embeddings produced by Deep Learning techniques. Such embeddings are vectorial representations of the words within context. For example, because the words "clustering" and "classification" are used frequently to describe machine learning tasks, they would have similar embeddings. Thus, one can match a resume that mentions one of the terms to a job description mentioning the other, even if approximately. Similarly, merging the word embeddings for the terms "machine" and "learning" will result in a vector that is similar to the embeddings of "clustering" and "classification", allowing for a match where the previous methods would fail._x000D_
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_x000D_, agreement title english : Deep learning and word embeddings for HR processes, agreement title french : Deep learning and word embeddings for HR processes, agreement number : EGP, agreement value : 25000.0, agreement start date : 2017-06-14, agreement end date : nan, description english : nan, description french : nan, expected results english : nan, expected results french : nan, additional information english : Grant or Award spanning more than one fiscal year. (2017-2018 to 2018-2019), additional information french : Subvention ou bourse octroyée s'appliquant à plus d'un exercice financier. (2017-2018 à 2018-2019), owner organization : nserc-crsng, owner organization title : Natural Sciences and Engineering Research Council of Canada | Conseil de recherches en sciences naturelles et en génie du Canada