Due to the increasing number of graduates, many applicants experience the situation about finding a job, and employers experience difficulty filtering job applicants, which might negatively impact their effectiveness. However, most job-hunting websites lack job recommendation and CV filtering or ranking functionality, which are not integrated into the system. Thus, a smart job hunter combined with the above functionality will be conducted in this project, which contains job recommendations, CV ranking and even a job dashboard for skills and job applicant functionality. Job recommendation and CV ranking starts from the automatic keyword extraction and end with the Job/CV ranking algorithm. Automatic keyword extraction is implemented by Job2Skill and the CV2Skill model based on Bert. Job2Skill consists of two components, text encoder and Gru-based layers, while CV2Skill is mainly based on Bert and fine-tunes the pre-trained model by the Resume- Entity dataset. Besides, to match skills from CV and job description and rank lists of jobs and candidates, job/CV ranking algorithms have been provided to compute the occurrence ratio of skill words based on TFIDF score and match ratio of the total skill numbers. Besides, some advanced features have been integrated into the website to improve user experiences, such as the calendar and sweetalert2 plugin. And some basic features to go through job application processes, such as job application tracking and interview arrangement.

We'll need to tackle problems that span a variety of domains: machine learning, computer vision, data science and distributed systems.

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Azure Machine Learning (Azure ML) is a fully managed cloud service that enables you to easily build, deploy and share predictive analytics solutions. Azure ML allows you to create a predictive analytic experiment and then directly publish that as a web service. The web service API can be used in two modes: "Request Response" and "Batch Execution". A Request-Response Service (RRS) is a low-latency, highly scalable web service used to provide an interface to stateless models that have been created and deployed from an Azure Machine Learning Studio experiment. It enables scenarios where the consuming application expects a response in real-time.