  We implemented and evaluated a two-stage retrieval method for personalized
academic search in which the initial search results are re-ranked using an
author-topic profile. In academic search tasks, the user's own data can help
optimizing the ranking of search results to match the searcher's specific
individual needs. The author-topic profile consists of topic-specific terms,
stored in a graph. We re-rank the top-1000 retrieved documents using ten
features that represent the similarity between the document and the
author-topic graph. We found that the re-ranking gives a small but significant
improvement over the reproduced best method from the literature. Storing the
profile as a graph has a number of advantages: it is flexible with respect to
node and relation types; it is a visualization of knowledge that is
interpretable by the user, and it offers the possibility to view relational
characteristics of individual nodes.
