Abstract: Systems and methods for presenting content to a user are described. A trained neural network is stored in memory, defining input nodes representing respective attribute values of attribute types, and weights embodying strengths of connections between the input nodes and hidden nodes, as trained for the particular user. Sub-models of the neural network are defined from sets of input nodes of the same attribute type and a corresponding hidden state matrix of trained weights. A request for content assets is processed using a retrieved first sub-model corresponding to a query attribute type and second sub-model corresponding to a target attribute type, to determine relevancy parameters for the user. Content assets are selected for presentation by a media receiver device, based on the determined relevancy parameters. Other embodiments are also described and claimed.

Abstract: Systems and methods for presenting content to a user are described. A trained neural network is stored in memory, defining input nodes representing respective attribute values of attribute types, and weights embodying strengths of connections between the input nodes and hidden nodes, as trained for the particular user. Sub-models of the neural network are defined from sets of input nodes of the same attribute type and a corresponding hidden state matrix of trained weights. A request for content assets is processed using a retrieved first sub-model corresponding to a query attribute type and second sub-model corresponding to a target attribute type, to determine relevancy parameters for the user. Content assets are selected for presentation by a media receiver device, based on the determined relevancy parameters. Other embodiments are also described and claimed.