Context based personalized ranking in academic search

This thesis focuses on two problems related to academic information retrieval systems. The first part is dedicated to data sets for search engine evaluation. We propose an automatic method to generate queries from the content of a scientific article and evaluate the relevant results. A test collection is generated, but its power to discriminate between relevant and non relevant results is limited. In the second part of the thesis Scopus performance is improved through personalization. We focus on the academic background of researchers that interact with Scopus since information about their academic profile is already available.