11. A method for identifying content for users within a social network, the method steps comprising: a server obtaining via network-based communication with remote, distributed devices inputs from a plurality of users, the inputs including ratings for items from the users and their corresponding contextual moods; wherein the ratings are user-specified and dependent upon a content of uniform resource indicators (URIs); wherein the moods are user-specified rater states independent of the URI content, and wherein the moods denote a context-specific preference; the server communicating with a client device including a graphical user interface (GUI) over an electronic social network and the server providing addressable URIs over the network; the server automatically receiving and storing the ratings from the GUI, tracking the URIs, using the ratings to create a preference model for the URIs, using social graph data, and using the preference model to suggest additional URIs; wherein the stored ratings include at least one record having a rated item URI, a rated item rater having a unique identification, at least one rated item rating value provided by the rated item rater, and at least one metadatum including the moods for creating subsets of ratings; and wherein the social graph data includes information about what content of URIs a user has shared with their friends, what content of URIs their friends have shared with the user, what content of URIs the user or their friends have liked or disliked, personal connections, business connections, and combinations thereof; wherein all ratings and social graph data are available to the preference model; the server combining the sets of ratings to form a combined datasets; the server identifying the items where ratings collisions exist; the server treating ratings collisions automatically to generate a treated data subset of the combined dataset; the server identifying items to suggest to users or enabling access to specific content distribution.
12. The method of claim 11 , further including the step of identifying users to receive the identified suggested items.