The last decade has witnessed a fundamental paradigm shift on how information content is distributed among people. Nowadays, an increasing number of platforms allow everyone to participate both in information production and information consumption. The phenomenon has been coined as democratization of content. However, as the opportunities to find relevant information and relevant audience increases, so does the complexity of a system that would allow suppliers and consumers to meet in the most efficient way. Our motivation is building a “featured item” component for social-media applications. Such a component would provide recommendations to consumers each time they login the system. The existing system follows either collaborative filtering or content based filtering. Collaborative filtering methods are based on collecting and analyzing a large amount of information on user’s behaviours, activities or preferences and predicting what users will like based on their similarity to other users. Content-based filtering methods are based on a description of the item and a profile of the user's preference. Both of these methods require input from the user in the form of ratings or other user's likes. But social content matching takes into account only the user's preferences and also the capacity constraints. For each item 't' and each user 'u', consider constraints on the maximum number of edges that t and u can participate in the matching. These capacity constraints can be estimated by the activity of each user and the relative frequency with which items need to be delivered. Here we introduce the concept called b-matching goal is to find a matching that satisfies all capacity constraints and maximizes the total weight of the edges in the matching. The result of b-matching is the set of songs that are to be recommended to the user based on his likes.