Enhanced query performance prediction for information retrieval systems

Abstract

Techniques are disclosed for query performance prediction (QPP) in the fusion-based retrieval setting. Symmetric list similarity measures used in traditional QPP techniques do not properly account for relevance-dependent aspects of the relationship between a given (base) reference list generated using an information retrieval technique and a final fused list generated using a fusion technique, as such a relationship is actually asymmetric. Embodiments more properly model the asymmetric relationship of reference and fused lists using an asymmetric co-relevance model that estimates, assuming a reference list contains relevant information, the odds that the fused list will be observed. In particular, the asymmetric co-relevance between a reference list and a fused list may be determined by adjusting a symmetric co-relevance of the reference list and the fused list using an odds ratio between the symmetric co-relevance of the reference list and the fused list to the reference list's own relevance.