System and method for automated recommendation generation from communication content and application thereof

Machine readable and non-transitory medium having information recorded thereon for product recommendation, wherein the information, when read by a machine, causes the machine to perform following steps:

• receiving communication content from a communication platform operated by one of a plurality of service providers operating on one of a plurality of platforms to facilitate creation of the communication content;
• identifying automatically, via machine learning, information related to a product mentioned on a webpage obtained based on a link included in the communication content;
• identifying, based on machine-learned demographics/interests/platforms (DIP) targeting models, a plurality of targets associated with each of the one or more service providers as intended targeted audience for the recommendation of the product;
• generating a recommendation of the product based on the information related to the product, wherein the recommendation comprises the identified information related to the product;
• sending the recommendation to one or more of the plurality of service providers operating on respective platforms for distributing the recommendation of the product to the plurality of targets associated with each of the one or more service providers;
• and
• updating, based on user activities associated with the product received from the one or more service providers, the demographics/interests/platforms (DIP) targeting models.

A system for product recommendation, comprising:

• a product recommendation generator implemented by a processor and configured for receiving communication content from a communication platform operated by one of a plurality of service providers operating on one of a plurality of platforms to facilitate creation of the communication content,
• identifying automatically, via machine learning, information related to a product mentioned on a webpage obtained based on a link included in the communication content,
• identifying, based on machine-learned demographics/interests/platforms (DIP) targeting models, a plurality of targets associated with each of the one or more service providers as intended targeted audience for the recommendation of the product;
• and
• generating a recommendation of the product based on the information related to the product, wherein the recommendation comprises the identified information related to the product;
• and
• a recommendation card distribution engine implemented by a processor and configured for
• sending the recommendation to one or more of the plurality of service providers operating on respective platforms for distributing the recommendation of the product to the plurality of targets associated with each of the one or more service providers, and
• updating, based on user activities associated with the product received from the one or more service providers, the demographics/interests/platforms (DIP) targeting models.