System, method, and non-transitory computer readable medium for generating electronic communications using transformer-based sequential and real-time top in type models

A computer-implemented method, comprising:

• receiving a request to generate an electronic communication, wherein the request includes a user identifier and a prior interaction;
• obtaining historical interaction data associated with the user identifier;
• inputting at least a portion of the historical interaction data into a hybrid sequential model to generate a set of hybrid sequential element recommendations selected from a catalog of items, the hybrid sequential model comprising a sequential sub-model that includes one or more self-attentive transformer layers trained to select a set of sequential elements and a re-ranking sub-model that includes one or more affinity layers trained to re-rank the set of sequential elements based on an affinity score for the user identifier and the set of sequential elements to generate the set of hybrid sequential elements;
• inputting the prior interaction into an affinity model that includes one or more affinity layers trained to generate a set of affinity element recommendations;
• generating, using an intent model having one or more trained intent layers, a set of relevant content elements including at least one of the set of hybrid sequential element recommendations and at least one of the set of affinity element recommendations;
• generating the electronic communication to include (i) a first portion comprising interface elements that present information related to the prior interaction and (ii) a second portion comprising the set of relevant content elements that are simultaneously relevant to the first portion and to the user identifier;
• and
• transmitting the electronic communication to a user device associated with the user identifier.

A non-transitory computer readable medium having instructions stored thereon, wherein the instructions, when executed by at least one processor, cause at least one device to perform operations comprising:

• receiving a request to generate an electronic communication, wherein the request includes a user identifier and a prior interaction;
• obtaining historical interaction data associated with the user identifier;
• inputting at least a portion of the historical interaction data into a hybrid sequential model to generate a set of hybrid sequential element recommendations selected from a catalog of items, the hybrid sequential model comprising a sequential sub-model that includes one or more self-attentive transformer layers trained to select a set of sequential elements and a re-ranking sub-model that includes one or more affinity layers trained to re-rank the set of sequential elements based on an affinity score for the user identifier and the set of sequential elements to generate the set of hybrid sequential elements;
• inputting the prior interaction into an affinity model that includes one or more affinity layers trained to generate a set of affinity element recommendations;
• generating, using an intent model having one or more trained intent layers, a set of relevant content elements including at least one of the set of hybrid sequential element recommendations and at least one of the set of affinity element recommendations;
• generating the electronic communication to include (i) a first portion comprising interface elements that present information related to the prior interaction and (ii) a second portion comprising the set of relevant content elements that are simultaneously relevant to the first portion and to the user identifier;
• and
• transmitting the electronic communication to a user device associated with the user identifier.