Abstract: Systems, apparatuses and methods provide technology that identifies a first post that is submitted to a first group of a social network. The technology identifies that the first post is a cross-pollination candidate, identifies a second group of the social network, generates a first vector that is to represent one of the first post or the first group, generates a second vector that is to represent the second group, determines whether the second group matches a cross-pollination criteria based on a comparison of the first vector to the second vector, and determines whether to automatically generate a second post based on the first post, and submit the second post to the second group based on whether the second group matches the cross-pollination criteria.

Abstract: The present disclosure provides systems and methods for applying machine learning modules to assess social networking relationships and generate recommendations. In various embodiments, user information, product information, social networking information may be gathered to generate a social networking graph. A first machine learning module may generate recommendations, such as a product recommendation based on information about a user and the social networking graph. Aspects include training the machine learning model based on product recommendations, previous purchases, and the like. A second machine learning module may generate a weighting characteristic associated with a user and a weighting characteristic associated with a product. The weightings may be applied to generate product recommendations.