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The goal of rank fusion in information retrieval (IR) is to deliver a singleoutput list from multiple search results. Improving performance by combiningthe outputs of various IR systems is a challenging task. A central point is thefact that many non-obvious factors are involved in the estimation of relevance,inducing nonlinear interrelations between the data. The ability to modelcomplex dependency relationships between random variables has becomeincreasingly popular in the realm of information retrieval, and the need tofurther explore these dependencies for data fusion has been recentlyacknowledged. Copulas provide a framework to separate the dependence structurefrom the margins. Inspired by the theory of copulas, we propose a newunsupervised, dynamic, nonlinear, rank fusion method, based on a nestedcomposition of non-algebraic function pairs. The dependence structure of themodel is tailored by leveraging query-document correlations on a per-querybasis. We experimented with three topic sets over CLEF corpora fusing 3 and 6retrieval systems, comparing our method against the CombMNZ technique and othernonlinear unsupervised strategies. The experiments show that our fusionapproach improves performance under explicit conditions, providing insightabout the circumstances under which linear fusion techniques have comparableperformance to nonlinear methods.

Exploiting Hierarchical Dependence Structures for Unsupervised Rank Fusion in Information Retrieval