D-Rank: a framework for score aggregation in specialized search

In this paper we present an approach to score aggregation for specialized search systems. In our work we focus on document ranking in scientific publication databases. We work with the collection of scientific publications of the CERN Document Server. This paper reports on work in progress and describes rank aggregation framework with score normalization. We present results that we obtained with aggregations based on logistic regression using both ranks and scores. In our experiment we concluded that score-based aggregation favored performance in terms of Average Precision and Mean Reciprocal Rank, while rank-based aggregation favored document discovery.