More and more web users keep up with newest information through information streams. One prominent example of an information stream is the popular micro-blogging website Twitter. In this paper we studied recommending content on Twitter to alleviate information overload and better direct user attention. In a modular approach, we explored three separate dimensions in designing such a recommender: selecting promising subsets of content for consideration, modeling user topic interest, and leveraging social process. We implemented 12 possible recommendation engines in the design space we formulated, and deployed them to a recommender service on the web to gather feedback from real Twitter users. The best performing algorithm improved the percentage of interesting content to 72% from a baseline of 33%. We conclude this work by discussing implications of our result and how our recommender design can generalize to other information streams.