Hybrid meta learning for agnostic recommender platforms

A system comprising a memory, computer readable instructions, and one or more processors for executing the computer readable instructions, the computer readable instructions controlling the one or more processors to perform operations comprising:

• receiving, by a global block ranker of a hybrid meta learning recommendation service, a request corresponding to an entity in a network;
• generating, by a meta block encoder of the hybrid meta learning recommendation service at a first cadence decoupled from the request, a meta embedding of an entity-specific meta feature of the entity;
• aggregating the meta embedding with one or more non-meta features at a second cadence responsive to the request, the one or more non-meta features bypassing the meta block encoder;
• inputting the aggregated meta embedding and one or more non-meta features to the global block ranker;
• generating, by the global block ranker, a prediction score for each candidate of one or more candidates corresponding to the request;
• and
• returning, responsive to receiving the request and by the global block ranker, a response comprising a candidate of the one or more candidates using the prediction score.

A computer program product comprising a computer readable storage medium having program instructions embodied therewith, the program instructions executable by one or more processors to cause the one or more processors to perform operations comprising:

• receiving, by a global block ranker of a hybrid meta learning recommendation service, a request corresponding to an entity in a network;
• generating, by a meta block encoder of the hybrid meta learning recommendation service at a first cadence decoupled from the request, a meta embedding of an entity-specific meta feature of the entity;
• aggregating the meta embedding with one or more non-meta features at a second cadence responsive to the request, the one or more non-meta features bypassing the meta block encoder;
• inputting the aggregated meta embedding and one or more non-meta features to the global block ranker;
• generating, by the global block ranker, a prediction score for each candidate of one or more candidates corresponding to the request;
• and
• returning, responsive to receiving the request and by the global block ranker, a response comprising a candidate of the one or more candidates using the prediction scores.