Social networks such as Facebook and Twitter offer a huge opportunity to tap the collective wisdom (both published and yet to be published) of all the participating users in order to address the information needs of individual users in a highly contextualized fashion using rich user-specific information. Realizing this opportunity, however, requires addressing two key limitations of current social networks: (a) difficulty in discovering relevant content beyond the immediate neighborhood, (b) lack of support for information filtering based on semantics, content source and linkage. We propose a scalable framework for constructing smart news feeds based on predicting user-post relevance using multiple signals such as text content and attributes of users and posts, and various user-user, post-post and user-post relations (e.g. friend, comment, author relations). Our solution comprises of two steps where the first step ensures scalability by selecting a small set of user-post dyads with potentially interesting interactions using inverted feature indexes. The second step models the interactions associated with the selected dyads via a joint latent factor model, which assumes that the user/post content and relationships can be effectively captured by a common latent representation of the users and posts. Experiments on a Facebook dataset using the proposed model lead to improved precision/recall on relevant posts indicating potential for constructing superior quality news feeds. © 2011 Authors.