TECHNIQUES FOR ADAPTIVE MULTI-LEVEL RECOMMENDATION USING HIERARCHICAL MIXTURE-OF-EXPERTS FRAMEWORK
개요
발명자
Maryam ESMAEILI; Justin Derrick BASILICO; Christoph KOFLER; Inbar NAOR; Jiangwei PAN; Jin WANG
IPC 분류
CPC 분류
Techniques for adaptive multi-level recommendation include processing input features to generate pre-processed input features, processing the input features and the pre-processed input features using a plurality of expert models to generate expert outputs, mixing the input features and the expert outputs to generate mixed expert outputs, processing the mixed expert outputs using a first model of the hierarchical model to generate intermediate outputs, and processing the mixed expert outputs and the intermediate outputs using a second model of the hierarchical model to generate a final output.
원문 (중국어)
Techniques for adaptive multi-level recommendation include processing input features to generate pre-processed input features, processing the input features and the pre-processed input features using a plurality of expert models to generate expert outputs, mixing the input features and the expert outputs to generate mixed expert outputs, processing the mixed expert outputs using a first model of the hierarchical model to generate intermediate outputs, and processing the mixed expert outputs and the intermediate outputs using a second model of the hierarchical model to generate a final output.