Unified recommendation system

A system, comprising:

• one or more processors;
• and
• a memory storing program instructions that, when executed by the one or more processors, cause the one or more processors to at least:
• for each content item of a plurality of content items:
• determine a set of user engagement history data for a user to which content items are to be presented;
• determine a set of content item information data for the content item;
• determine a set of contextual relevance data, including:
• a query context associated with a first output channel of a plurality of output channels, and
• a content relevance based on existing content items corresponding to the query context;
• and
• generate a plurality of engagement scores by providing the set of contextual relevance data and at least one of the set of user engagement history data or the set of content item information data to a machine learning model (“ML model”), the ML model trained on user engagement data across the plurality of output channels, each of the plurality of engagement scores corresponding to an engagement action of a plurality of engagement actions;
• and
• provide a ranked list of the content items to the first output channel, wherein the plurality of content items are ranked based at least in part on the plurality of engagement scores generated for each content item.

A method, comprising:

• receiving a content item request for a first output channel of a plurality of output channels;
• obtaining candidate content items potentially responsive to the content item request;
• for each candidate content item:
• determining a set of user engagement history data corresponding to a user to which content items are to be presented;
• determining a set of candidate content item information data corresponding to the candidate content item;
• determining a set of contextual relevance data, including:
• a query context associated with the first output channel;
• a content relevance based on existing content items corresponding to the query context;
• generating a plurality of engagement scores by providing the set of user engagement history data, the set of candidate content item information data, and the set of contextual relevance data to a machine learning model (“ML model”), the ML model trained on user engagement data across the plurality of output channels, each of the plurality of engagement scores corresponding to an engagement action of a plurality of engagement actions;
• and
• generating a ranking score by combining at least two of the engagement scores;
• and
• providing ranked content items to the first output channel for presentation based at least in part on the ranking scores.