Generalized Deep Mixed Models

We introduce generalized deep mixed model (GDMix), a class of machine learning models for large-scale recommender systems that combines the power of deep neural networks and the efficiency of logistic regression. GDMix leverages state-of-the-art deep neural networks (DNNs) as the global models (fixed effects), and further improves the performance by adding entity-specific personalized models (random effects). For instance, the click response from a particular user m to a job posting j may consist of contributions from a DNN model common to all users and job postings, a model specific to the user m and a model specific to the job j. GDMix models not only possess powerful modeling capabilities but also enjoy high training efficiency especially for web-scale recommender systems. We demonstrate the capabilities by detailing their use in Feed and Ads recommendation at LinkedIn. The source code for the GDMix training framework is available at https://github.com/linkedin/gdmix https://github.com/linkedin/gdmix under the BSD-2-Clause License.