Reference number : GC-2017-Q1-00545, amendment number : , agreement type : G, recipient legal name : Du, Weichang (University of New Brunswick)|Du, Weichang (Université du Nouveau-Brunswick), recipient country : CA, recipient province : nan, recipient city : New Brunswick|Nouveau-Brunswick, recipient postal code : nan, program name english : Engage Plus Grants for Universities, program name french : Subvention d'engagement partenarial Plus pour les universités, program purpose english : In our earlier engagement with the industrial partner, we effectively developed methods that produced quality_x000D_
recommendations to job seekers. We built a wide host of methods, ranging from syntactical and statistical_x000D_
distribution of words and concepts to more complex language modeling techniques, as well as the use of_x000D_
unsupervised learning of continuous representations of job postings through word and paragraph and document_x000D_
embeddings, were used. However, once integrated and employed on BCJobs, it was observed that higher_x000D_
quality recommendations do not necessarily result in more job applications. Based on this finding, in this_x000D_
follow-up project, our primary objective will be on understanding user behavior from interaction logs to_x000D_
address the following question: 'what are the parameters or conditions that impact user engagement with the_x000D_
BCJobs platform?' with the aim of discovering why users decide not to further engage with BCJobs after their_x000D_
first job posting view. This will also require us to understand what encouraged those users who did, in fact,_x000D_
decide to engage and eventually applied for a given job. The primary research methods used for this project_x000D_
will be based on performing randomized controlled studies to determine the impact of various factors on user_x000D_
engagement. The research work and required development will be carried out in close concert with the_x000D_
industrial partner BC Jobs Online Inc., program purpose french : In our earlier engagement with the industrial partner, we effectively developed methods that produced quality_x000D_
recommendations to job seekers. We built a wide host of methods, ranging from syntactical and statistical_x000D_
distribution of words and concepts to more complex language modeling techniques, as well as the use of_x000D_
unsupervised learning of continuous representations of job postings through word and paragraph and document_x000D_
embeddings, were used. However, once integrated and employed on BCJobs, it was observed that higher_x000D_
quality recommendations do not necessarily result in more job applications. Based on this finding, in this_x000D_
follow-up project, our primary objective will be on understanding user behavior from interaction logs to_x000D_
address the following question: 'what are the parameters or conditions that impact user engagement with the_x000D_
BCJobs platform?' with the aim of discovering why users decide not to further engage with BCJobs after their_x000D_
first job posting view. This will also require us to understand what encouraged those users who did, in fact,_x000D_
decide to engage and eventually applied for a given job. The primary research methods used for this project_x000D_
will be based on performing randomized controlled studies to determine the impact of various factors on user_x000D_
engagement. The research work and required development will be carried out in close concert with the_x000D_
industrial partner BC Jobs Online Inc., agreement title english : Semantics based text matching for unstructured text documents, agreement title french : Semantics based text matching for unstructured text documents, agreement number : EGP2, agreement value : 12500.0, 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