Homeland security intelligence analysts need help finding relevant information quickly in a rapidly increasing volume of incoming raw data. Many different AI techniques are needed to handle this deluge of data. This paper describes initial investigations in the application of recommender systems to this problem. It illustrates various recommender systems technologies and suggests scenarios for how recommender systems can be applied to support an analyst. Since unclassified data on the search behavior of analysts is hard to obtain we have built a proof-of-concept demo using analogous search behavior data in the computer science domain. The proof-of-concept collaborative recommender system that we developed is described.