Systems and methods for generating scalable personalized recommendations based on dynamic and customized content selections and modeling of the content selections

A non-transitory computer-readable medium, storing a plurality of processor executable instructions to:

• maintain a plurality of recommendation configurations, wherein each respective recommendation configuration includes:
• a respective set of selector criteria based on which the respective recommendation configuration is selected from the plurality of recommendation configurations,
• a respective set of pre-boost parameters specifying criteria based on which content can be selected from a plurality of content and can further be prioritized,
• a respective set of models to apply to content selected and prioritized according to the respective set of pre-boost parameters, wherein different recommendation configurations include different sets of models;
• receive a request comprising a query and an identifier that identifies a requestor issuing the request;
• compare the query and the identifier of the requestor to the respective sets of selector criteria associated with the plurality of recommendation configurations;
• select a particular recommendation configuration based on comparing the query and the identifier of the requestor to a the respective sets of selector criteria;
• retrieve a set of content from a plurality of accessible content based on each content in the set of content satisfying the query and the set of pre-boost parameters included in the particular recommendation configuration;
• prioritize the retrieved set of content in a first order based on the set of pre-boost parameters included in the particular recommendation configuration;
• apply the respective set of models, associated with the particular recommendation configuration, to the set of content;
• and
• present recommended content in a second order, that is different than the first order, based on applying the respective set of models, associated with the particular recommendation configuration that was selected based on the comparing, to the retrieved set of content.

A method, comprising:

• maintaining a plurality of recommendation configurations, wherein each respective recommendation configuration includes:
• a respective set of selector criteria based on which the respective recommendation configuration is selected from the plurality of recommendation configurations,
• a respective set of pre-boost parameters specifying criteria based on which content can be selected from a plurality of content and can further be prioritized,
• a respective set of models to apply to content selected and prioritized according to the respective set of pre-boost parameters, wherein different recommendation configurations include different sets of models;
• receiving a request comprising a query and an identifier that identifies a requestor issuing the request;
• comparing the query and the identifier of the requestor to the respective sets of selector criteria associated with the plurality of recommendation configurations;
• selecting a particular recommendation configuration based on comparing the query and the identifier of the requestor to the respective sets of selector criteria;
• retrieving a set of content from a plurality of accessible content based on each content in the set of content satisfying the query and the set of pre-boost parameters included in the particular recommendation configuration;
• prioritizing the retrieved set of content in a first order based on the set of pre-boost parameters included in the particular recommendation configuration;
• applying the respective set of models, associated with the particular recommendation configuration, to the set of content;
• and
• presenting recommended content in a second order, that is different than the first order, based on applying the respective set of models, associated with the particular recommendation configuration that was selected based on the comparing, to the retrieved set of content.