Microsoft Technology Licensing, LLC

Shan Li

Jaewon Yang

Baoxu Shi

Qi He

Fei Chen

Feng Guo

Mustafa Emre Kazdagli

Described herein are techniques for using a graph neural network to encode online job postings as embeddings. First, an input graph is defined by processing one or more rules to discover edges that connect nodes in an input graph, where the nodes of the input graph represent job postings or standardized job attributes, and the edges are determined based on analyzing a log of user activity directed to online job postings. Next, a graph neural network (GNN) is trained based on an edge prediction task. Finally, once trained, the GNN is used to derive node embeddings for the nodes (e.g., job postings) of the input graph, and in some instances, new online job postings not represented in the original input graph.

Encoding a job posting as an embedding using a graph neural network

In an example embodiment, a screening question-based online screening mechanism is provided to assess job applicants automatically. More specifically, job-specific questions are automatically generated and asked to applicants to assess the applicants using the answers they provide. Answers to these questions are more recent than facts contained in a user profile and thus are more reliable measures of an appropriateness of an applicant's skills for a particular job.

Machine learned model framework for screening question generation

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Shan Li