Semantics based text matching for unstructured text documents

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In our earlier engagement with the industrial partner, we effectively developed methods that produced qualityrecommendations to job seekers. We built a wide host of methods, ranging from syntactical and statisticaldistribution of words and concepts to more complex language modeling techniques, as well as the use ofunsupervised learning of continuous representations of job postings through word and paragraph and documentembeddings, were used. However, once integrated and employed on BCJobs, it was observed that higherquality recommendations do not necessarily result in more job applications. Based on this finding, in thisfollow-up project, our primary objective will be on understanding user behavior from interaction logs toaddress the following question: 'what are the parameters or conditions that impact user engagement with theBCJobs platform?' with the aim of discovering why users decide not to further engage with BCJobs after theirfirst job posting view. This will also require us to understand what encouraged those users who did, in fact,decide to engage and eventually applied for a given job. The primary research methods used for this projectwill be based on performing randomized controlled studies to determine the impact of various factors on userengagement. The research work and required development will be carried out in close concert with theindustrial partner BC Jobs Online Inc.