More and more web users keep up with newest information through
information streams such as the popular microblogging website
Twitter. In this paper we studied content recommendation on Twitter to
better direct user attention. In a modular approach, we explored three
separate dimensions in designing such a recommender: content sources,
topic interest models for users, and social voting. We implemented 12
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 the implications of our recommender design and how
our design can generalize to other information streams.