Machine Intelligence for Matching People and Jobs
For many people, their job is no longer just a source of income: it is an avenue for personal growth and accomplishments. This puts additional pressure both on the job seeker and on hiring managers or HR professionals. In this talk I will briefly discuss how Machine Learning and Deep Learning are revolutionizing the tooling for matching people and jobs. On the document understanding side, I will show how LSTM neural networks are improving the quality and robustness of Textkernel’s resumes and job ads parsing product. In addition, I will present our recent efforts in building a language-independent resume parsing system akin to Google’s universal machine translation system. I will also discuss how Siamese architectures can be used to learn similarities between job titles (e.g. “java developer” vs “java programmer”). On the searching and matching side, I will describe how relevance can be improved and customized via Learning to Rank.
Mihai Rotaru is the Head of R&D at Textkernel where he is responsible for the research agenda and for coordinating the joint research efforts with the parent company, CareerBuilder. In this role he is guiding 3 teams: the Document Understanding team which builds the resume and job parsing product, the Search R&D team which is improving the Search & Match product and the Ontology team which builds the HR domain knowledge graph. Originally from Romania, he joined Textkernel in 2008 after obtaining a PhD degree in Computer Science at University of Pittsburgh, USA. Among others, he is interested in Machine Learning, NLP, Deep Learning and how these new technologies can be applied in industry.