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Understandability Biased Evaluation for Information Retrieval

  • Guido ZucconEmail author
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9626)

Abstract

Although relevance is known to be a multidimensional concept, information retrieval measures mainly consider one dimension of relevance: topicality. In this paper we propose a method to integrate multiple dimensions of relevance in the evaluation of information retrieval systems. This is done within the gain-discount evaluation framework, which underlies measures like rank-biased precision (RBP), cumulative gain, and expected reciprocal rank. Albeit the proposal is general and applicable to any dimension of relevance, we study specific instantiations of the approach in the context of evaluating retrieval systems with respect to both the topicality and the understandability of retrieved documents. This leads to the formulation of understandability biased evaluation measures based on RBP. We study these measures using both simulated experiments and real human assessments. The findings show that considering both understandability and topicality in the evaluation of retrieval systems leads to claims about system effectiveness that differ from those obtained when considering topicality alone.

Keywords

User Model Information Retrieval System Readability Score Gain Function Discount Function 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Notes

Acknowledgements

The author is thankful to Bevan Koopman, Leif Azzopardi, Joao Palotti, Peter Bruza, Alistair Moffat and Lorraine Goeuriot for their comments on the ideas proposed in this paper.

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Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  1. 1.Queensland University of Technology (QUT)BrisbaneAustralia

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