TECHNIQUES FOR ADAPTIVE MULTI-LEVEL RECOMMENDATION USING HIERARCHICAL MIXTURE-OF-EXPERTS FRAMEWORK

原文（中文）

Techniques for training a hierarchical model include concurrently training a first model and a second model of the hierarchical model using first training data to update first parameters of the first model and second parameters of the second model, wherein output from the first model is provided to the second model. Upon determining that a performance metric has met one or more criteria, the first parameters are frozen to generate frozen first parameters. The second model is then further trained using second training data, wherein the second training data is presented to the first model with the frozen first parameters and the second model.