  The goal of rank fusion in information retrieval (IR) is to deliver a single
output list from multiple search results. Improving performance by combining
the outputs of various IR systems is a challenging task. A central point is the
fact that many non-obvious factors are involved in the estimation of relevance,
inducing nonlinear interrelations between the data. The ability to model
complex dependency relationships between random variables has become
increasingly popular in the realm of information retrieval, and the need to
further explore these dependencies for data fusion has been recently
acknowledged. Copulas provide a framework to separate the dependence structure
from the margins. Inspired by the theory of copulas, we propose a new
unsupervised, dynamic, nonlinear, rank fusion method, based on a nested
composition of non-algebraic function pairs. The dependence structure of the
model is tailored by leveraging query-document correlations on a per-query
basis. We experimented with three topic sets over CLEF corpora fusing 3 and 6
retrieval systems, comparing our method against the CombMNZ technique and other
nonlinear unsupervised strategies. The experiments show that our fusion
approach improves performance under explicit conditions, providing insight
about the circumstances under which linear fusion techniques have comparable
performance to nonlinear methods.

Information Retrieval