| CPC G06N 20/10 (2019.01) [G06F 18/214 (2023.01); G06F 18/217 (2023.01); G06N 20/20 (2019.01); G06Q 10/1053 (2013.01)] | 16 Claims |
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1. A system comprising:
a computer-readable medium having instructions stored thereon, which, when executed by a processor, cause the system to:
obtain training data, the training data comprising values for a plurality of different features;
train a global machine learned model using a first machine learning algorithm by feeding the training data into the first machine learning algorithm during a fixed effect training process, the first machine learning algorithm being a deep learning machine learning algorithm that utilizes a factorization machine with L2 norm reduction to divide calculations made by the factorization machine into a portion that can be precomputed and a portion that cannot be precomputed;
train a first random effects machine learned model by feeding a subset of the training data into a second machine learning algorithm, the subset of the training data being limited to training data corresponding to a particular value of one of the plurality of different features;
feed a first feature vector for a first document into the global machine learned model, producing a first score, the first document comprising a job posting from an online service;
feed a second feature vector for the first document into the first random effects machine learned model, producing a second score;
combine the first score and the second score into a ranking score, the ranking score used to rank the first document against other documents, the other documents comprising job postings from the online service; and
based on the ranking score, serving, to a graphical user interface (GUI) on a user device, the first document.
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