9. A method for creating a preference vector within a social network generated from ratings and/or moods of a plurality of users, the method steps comprising: a server obtaining via network-based communication with remote, distributed devices inputs from a plurality of users, wherein the plurality of users enter the inputs via a graphical user interface (GUI), 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 the GUI over a communications network with an electronic social network; the server providing addressable URIs over the communications network; the server automatically receiving ratings over the communications network via the GUI, automatically storing the ratings, tracking the URIs, using the stored ratings, and using social graph data to create a preference model for the URIs, and using the preference model to suggest additional URIs; wherein the stored ratings include at least one record having a rated item URI, a rater having a unique identification, at least one rating value provided by each of the users, and at least one metadatum including the moods for creating subsets of ratings; and wherein 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 identifying the multiple subsets of ratings for items to be used; the server automatically combining the multiple subsets of ratings and the treated data subset with social graph data to form a combined dataset; the server automatically identifying ratings collisions; the server treating the ratings collisions to generate a treated data subset of the combined dataset; and the server generating a preference vector based on the treated data subset.
10. The method of claim 9 , wherein the step of combining the treated data subset with social graph data includes one of: adjusting the choice of which items users are more likely to prefer based on data and/or ratings corresponding to the user friends in the social network, and/or filtering to avoid suggesting content that the user has already seen through the social network and/or user's friends.