Abstract: An example method includes dividing content into a normal set and an exclusive set, ranking content of the normal set based on a user preference vector, creating a prioritized list of tiers of content, publishing the prioritized list of tiers of content to the network and restricting access to certain tiers of the prioritized list of tiers of content, receiving a selection of a first content from a first tier (that is an unrestricted tier) associated with a first user account, determining a social engagement factor (SEF) for the first user account based on an amount of social media activity over the network associated with the first content and attributed to the first user account, and based on the SEF for the first user account, electronically enabling access to a higher priority tier of the prioritized list of tiers of content for the first user account.

Abstract: A dynamic group buying system that utilizes a neural network-based machine learning system to generate an optimized price campaign for a product or a product bundle while considering human product seller set bounds consisting of a variable combination of reserve quantity of product, minimum sales price and list price, duration of the campaign, number of levels and maximum price. As an extension of the machine learning capabilities, the system includes a recommendation system that aids the seller to make informed, decisions on factors such as what product(s) bundle should be added to the campaign, what is the ideal time and duration of the campaign, for example. The system is designed to optimize the campaign creation capabilities by analyzing the past product campaigns.