Personalization in professional academic search

In this paper, we investigated how academic search can profit from personalization by incorporating query history and background knowledge in the ranking of the results. We implemented both techniques in a language modelling framework, using the Indri search engine. For our experiments, we used the iSearch data collection, a large corpus of documents from the physics domain together with 65 search topics from scientists and students. We found that it is possible to improve academic search by taking into account query history. However, we have not been able to prove that terms extracted from the user's background data can improve academic search.