We have published the full recording of our RecSys in HR 2022 workshop, which we held September 22 in Seattle, WA, USA.
The video is 5h42m43s long, so to guide you, I provide you the following list of highlights (see the video description for timestamps that will allow you to instantly skip to the sections described below):
1️⃣ Our first keynote speaker, Robyn Rap, a data science leader at Indeed.com talks in depth about the importance of collaboration between #UX and Data Scientists in evaluating and developing search and recommendation systems. She provides a great (broad) overview of the challenges and differences of doing recsys in HR, compared to more common scenarios such as e-commerce or media. Great introduction into our deep field!
2️⃣ The panel, which includes Randstad’s Helen Hulsker, Carlos Castillo (ChaTo), Liangjie Hong (director of AI, engineering at LinkedIn) and the aforementioned Robyn Rap (still Indeed.com). The topics discussed by these experts: the role of HR Tech in the Global Labor Shortage, fair AI in Practice, multi-stakeholder development of HR Tech, and Regulation and Accountability.
3️⃣ Our second keynote speaker, Liangjie Hong, presents some of the foundational engineering work at LinkedIn that aims to serve many downstream AI applications, which revolves around a pipeline with (continuously updating) embedding representation for job seekers, jobs, and everything else, which are fused with LinkedIns (huge) Knowledge Graph.
4️⃣ There’s also a bunch of interesting paper presentations, e.g., a bunch from Indeed.com: Model Threshold Optimization for Segmented Job-Jobseeker Recommendation System (where the authors show a sneakpeek in their overall setup of recommendations at Indeed.com), Flexible Job Classification with Zero-Shot Learning by thomas lake, which shows how to use off-the-shelf transformer models for doing job classification. And Beyond human-in-the-loop: scaling occupation taxonomy at Indeed: where the authors show how they combine human intelligence with automation for scaling taxonomies across languages and markets. Finally, some interesting and very pragmatic/hands-on papers on skill extraction, e.g., Mike Zhang‘s Skill Extraction from Job Postings using Weak Supervision and Jens-Joris Decorte‘s Design of Negative Sampling Strategies for Distantly Supervised Skill Extraction.
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