Method and system for facilitating content recommendation to content viewers

A system, the system comprising:

• a memory for storing instructions;
• and
• a processor configured to execute the instructions and thereby cause the system, at least in part, to:
• access content viewer behavior data associated with a content viewer from a database associated with the system;
• classify the content viewer in a viewer cohort from a plurality of viewer cohorts based, at least in part, on the content viewer behavior data, the viewer cohort comprising a plurality of related content viewers;
• access and aggregate content viewer interaction data from the plurality of related content viewers in the viewer cohort, the content viewer interaction data comprising a popularity index of each recommended content item from a plurality of recommended content items that is being served on a plurality of electronic devices associated with the plurality of related content viewers by a digital platform server;
• compute via a machine learning model, a performance metric for each recommended content item of the plurality of recommended content items based, at least in part, on the content viewer interaction data;
• and
• determine an optimal content sequence for displaying the plurality of recommended content items based, at least in part, on the performance metrics for the plurality of recommended content items, the optimal content sequence indicating a sequence for displaying each recommended content item of the plurality of recommended content items on an electronic device of the content viewer;
• wherein to compute the performance metric the system is caused to perform a plurality of operations iteratively for a predefined threshold number of attempts, the plurality of operations comprising:
• compute a score for each recommended content item of the plurality of recommended content items based, at least in part, on the content viewer interaction data and a scoring policy;
• rank each recommended content item of the plurality of recommended content items based, at least in part, on the score related to the each recommended content item and a ranking policy;
• rearrange the plurality of ranked recommended content items at a predefined time interval based, at least in part, on a shuffling policy;
• facilitate the display of a plurality of rearranged recommended content items on User Interfaces (UIs) of the plurality of electronic devices associated with the plurality of related content viewers;
• and
• compute and update the content viewer interaction data from the plurality of related content viewers based, at least in part, on monitoring interactions of the plurality of related content viewers with the plurality of rearranged recommended content items.

A non-transitory computer-readable storage medium comprising computer-executable instructions that, when executed by at least a processor of a system, cause the system to perform a method comprising:

• accessing content viewer behavior data associated with a content viewer from a database associated with the system;
• classifying the content viewer in a viewer cohort from a plurality of viewer cohorts based, at least in part, on the content viewer behavior data, the viewer cohort comprising a plurality of related content viewers;
• accessing and aggregating content viewer interaction data from the plurality of related content viewers in the viewer cohort, the content viewer interaction data comprising a popularity index of each recommended content item from a plurality of recommended content items that is being served on a plurality of electronic devices associated with the plurality of related content viewers by a digital platform server;
• computing via a machine learning model, a performance metric for each recommended content item of the plurality of recommended content items based, at least in part, on the content viewer interaction data;
• and
• determining an optimal content sequence for displaying the plurality of recommended content items based, at least in part, on the performance metrics for the plurality of recommended content items, the optimal content sequence indicating a sequence for displaying each recommended content item of the plurality of recommended content items on an electronic device of the content viewer;
• wherein computing the performance metric comprises performing a plurality of operations iteratively for a predefined threshold number of attempts, the plurality of operations comprising:
• computing a score for each recommended content item of the plurality of recommended content items based, at least in part, on the content viewer interaction data and a scoring policy;
• ranking each recommended content item of the plurality of recommended content items based, at least in part, on the score related to the each recommended content item and a ranking policy;
• rearranging the plurality of ranked recommended content items at a predefined time interval based, at least in part, on a shuffling policy;
• facilitating the display of a plurality of rearranged recommended content items on User Interfaces (UIs) of the plurality of electronic devices associated with the plurality of related content viewers;
• and
• computing and updating the content viewer interaction data from the plurality of related content viewers based, at least in part, on monitoring interactions of the plurality of related content viewers with the plurality of rearranged recommended content items.

A computer-implemented method, comprising:

• accessing, by a system, content viewer behavior data associated with a content viewer from a database associated with the system;
• classifying, by the system, the content viewer in a viewer cohort from a plurality of viewer cohorts based, at least in part, on the content viewer behavior data, the viewer cohort comprising a plurality of related content viewers;
• accessing, by the system, a content library from a digital platform server, the content library including a plurality of content items;
• determining and accessing, by the system, a plurality of recommended content items from the plurality of content items of the content library for the content viewer based, at least in part, on the content viewer behavior data;
• computing, by the system via a machine learning model, a performance metric for each recommended content item of the plurality of recommended content items by performing a plurality of operations iteratively for a predefined threshold number of attempts, the plurality of operations comprising:
• facilitating, by the system, a display of at least a set of recommended content items from the plurality of recommended content items on UIs of electronic devices for a plurality of related content viewers,
• accessing and aggregating, by the system, content viewer interaction data from the plurality of related content viewers in the viewer cohort in relation to the plurality of recommended content items, the content viewer interaction data comprising a popularity index of each recommended content item from a plurality of recommended content items that is being served on a plurality of electronic devices associated with the plurality of related content viewers by a digital platform server,
• computing, by the system, a score for each recommended content item of the plurality of recommended content items based, at least in part, on the content viewer interaction data and a scoring policy,
• ranking, by the system, each recommended content item of the plurality of recommended content items based, at least in part, on the score related to the each recommended content item and a ranking policy,
• rearranging, by the system, the plurality of ranked recommended content items at a predefined time interval based, at least in part, on a shuffling policy,
• facilitating, by the system, the display of a plurality of rearranged recommended content items on User Interfaces (UIs) of the plurality of electronic devices associated with the plurality of related content viewers, and
• computing and updating, by the system, the content viewer interaction data from the plurality of related content viewers based, at least in part, on monitoring interactions of the plurality of related content viewers with the plurality of rearranged recommended content items;
• and
• determining, by the system, an optimal content sequence for displaying the plurality of recommended content items based, at least in part, on the performance metrics for the plurality of recommended content items, the optimal content sequence indicating a sequence for displaying each recommended content item of the plurality of recommended content items on an electronic device of the content viewer.