Microsoft Technology Licensing, LLC

Baoxu Shi

Qi He

Jaewon Yang

Feng Guo

Fei Chen

Shan Li

Mustafa Emre Kazdagli

Angie Zhu

Sufeng Niu

Minji Yoon

Techniques for implementing a performance-adaptive sampling strategy towards fast and accurate graph neural networks are provided. In one technique, a graph that comprises multiple nodes and edges connecting the nodes is stored. An embedding for each node is initialized, as well as a sampling policy for sampling neighbors of nodes. One or more machine learning techniques are used to train a graph neural network and learn embeddings for the nodes. Using the one or more machine learning techniques comprises, for each node: (1) selecting, based on the sampling policy, a set of neighbors of the node; (2) based on the graph neural network and embeddings for the node and the set of neighbors, computing a performance loss; and (3) based on a gradient of the performance loss, modifying the sampling policy.

Performance-adaptive sampling strategy towards fast and accurate graph neural networks

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

Techniques for scoring data items using a machine-learned model are provided. In one technique, multiple skills are identifying based on a job posting. Multiple attribute values of the job posting are identified. For each identified skill, multiple probabilities are identified, each probability corresponding to a different attribute value of the identified attribute values. The probabilities are input into a machine-learned model to generate multiple scores. Multiple skills of a candidate user are identified. An affinity score between the job posting and the candidate user is generated based the scores and the skills of the candidate user.

Generating a machine-learned model for scoring skills based on feedback from job posters

Techniques are provided for using machine learning techniques to analyze textual content. In one technique, a potential item is identified within a document. An analysis of the potential item is performed at multiple levels of granularity that includes two or more of a sentence level, a segment level, or a document level. The analysis produces multiple outputs, one for each level of granularity in the multiple levels of granularity. The outputs are input into a machine-learned model to generate a score for the potential item. Based on the score, the potential item is presented on a computing device. In response to user selection of the potential item, an association between the potential item and the document is created. The association may be used later to identify a set of users to which the document (or data thereof) is to be presented.

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Baoxu Shi