1. A method comprising, by one or more processors of a social-networking system of an online social network: accessing, by one or more of the processors, a social graph comprising a plurality of nodes and a plurality of edges connecting the nodes, each of the edges between two of the nodes representing a single degree of separation between them, the nodes comprising: a plurality of user nodes corresponding to a plurality of users associated with the online social network, respectively; and a plurality of concept nodes corresponding to a plurality of concepts associated with the online social network, respectively; accessing, by one or more of the processors, user information associated with each of the plurality of users relating to interactions with one or more of the plurality of concept nodes to generate a plurality of user-concept pairs; accessing, by one or more of the processors, user-concept scores for a first set of user nodes of the plurality of nodes, respectively, each user-concept score being with respect to particular user-concept pairs of the plurality of user-concept pairs, the particular user-concept pairs comprising a user node from the first set of user nodes that is connected by an edge to a concept node from the plurality of concept nodes; generating, by one or more of the processors, a recommendation-algorithm for estimating recommended user-concept scores for all user-concept pairs in the first set of user nodes and the plurality of concept nodes, the recommended user-concept scores being based on the accessed user-concept scores; calculating, by one or more of the processors, recommended user-concept scores for a random subset of user-concept pairs in a second set of user nodes of the plurality of user nodes and the plurality of concept nodes, the first set of user nodes being discrete from the second set of user nodes, the second set of user nodes comprising substantially all remaining user nodes of the plurality of user nodes, wherein the recommendation-algorithm computes the recommended user-concept scores by optimizing an objective function comprising a plurality of predicted rating functions, wherein each predicted rating function for a user-concept pair (u,i) comprises: a dot product of a user-score vector P(u) and concept-score vector Q(i); and bias values associated with user node u and concept node i; and sending, to one or more client systems of one or more users corresponding to user nodes of the second set of user nodes, recommendations for one or more concept nodes based on the calculated recommended user-concept scores for the second set of user nodes.