Digital books can significantly enhance the reading experience, providing many functions not available in printed books. In this paper we study a particular augmentation of digital books that provides readers with customized recommendations. We systematically explore the application of spreading activation over text and citation data to generate useful recommendations. Our findings reveal that for the tasks performed in our corpus, spreading activation over text is more useful than citation data. Further, fusing text and citation data via spreading activation results in the most useful recommendations. The fused spreading activation techniques outperform traditional text-based retrieval methods. Finally, we introduce a preliminary user interface for the display of recommendations from these algorithms.