A Meta-Evaluation of C/W/L/A Metrics: System Ranking Similarity, System Ranking Consistency and Discriminative Power

Recently, Moffat et al. proposed an analytic framework, namely C/W/L/A, for offline evaluation metrics. This framework allows information retrieval (IR) researchers to design evaluation metrics through the flexible combination of user browsing models and user gain aggregations. However, the statistical stability of C/W/L/A metrics with different aggregations is not yet investigated. In this study, we investigate the statistical stability of C/W/L/A metrics from the perspective of: (1) the system ranking similarity among aggregations, (2) the system ranking consistency of aggregations and (3) the discriminative power of aggregations. More specifically, we combined various aggregation functions with the browsing model of Precision, Discounted Cumulative Gain (DCG), Rank-Biased Precision (RBP), INST, Average Precision (AP) and Expected Reciprocal Rank (ERR), examing their performances in terms of system ranking similarity, system ranking consistency and discriminative power on two offline test collections. Our experimental result suggests that, in terms of system ranking consistency and discriminative power, the aggregation function of expected rate of gain (ERG) has an outstanding performance while the aggregation function of maximum relevance usually has an insufficient performance. The result also suggests that Precision, DCG, RBP, INST and AP with their canonical aggregation all have favourable performances in system ranking consistency and discriminative power; but for ERR, replacing its canonical aggregation with ERG can further strengthen the discriminative power while obtaining a system ranking list similar to the canonical version at the same time

An Axiomatic Analysis of Diversity Evaluation Metrics: Introducing the Rank-Biased Utility Metric

Many evaluation metrics have been defined to evaluate the effectiveness ad-hoc retrieval and search result diversification systems. However, it is often unclear which evaluation metric should be used to analyze the performance of retrieval systems given a specific task. Axiomatic analysis is an informative mechanism to understand the fundamentals of metrics and their suitability for particular scenarios. In this paper, we define a constraint-based axiomatic framework to study the suitability of existing metrics in search result diversification scenarios. The analysis informed the definition of Rank-Biased Utility (RBU) -- an adaptation of the well-known Rank-Biased Precision metric -- that takes into account redundancy and the user effort associated to the inspection of documents in the ranking. Our experiments over standard diversity evaluation campaigns show that the proposed metric captures quality criteria reflected by different metrics, being suitable in the absence of knowledge about particular features of the scenario under study.Comment: Original version: 10 pages. Preprint of full paper to appear at SIGIR'18: The 41st International ACM SIGIR Conference on Research & Development in Information Retrieval, July 8-12, 2018, Ann Arbor, MI, USA. ACM, New York, NY, US

Relevance Assessments for Web Search Evaluation: Should We Randomise or Prioritise the Pooled Documents? (CORRECTED VERSION)

In the context of depth-$k$ pooling for constructing web search test collections, we compare two approaches to ordering pooled documents for relevance assessors: the prioritisation strategy (PRI) used widely at NTCIR, and the simple randomisation strategy (RND). In order to address research questions regarding PRI and RND, we have constructed and released the WWW3E8 data set, which contains eight independent relevance labels for 32,375 topic-document pairs, i.e., a total of 259,000 labels. Four of the eight relevance labels were obtained from PRI-based pools; the other four were obtained from RND-based pools. Using WWW3E8, we compare PRI and RND in terms of inter-assessor agreement, system ranking agreement, and robustness to new systems that did not contribute to the pools. We also utilise an assessor activity log we obtained as a byproduct of WWW3E8 to compare the two strategies in terms of assessment efficiency.Comment: 30 pages. This is a corrected version of an open-access TOIS paper ( https://dl.acm.org/doi/pdf/10.1145/3494833

Statistical reform in information retrieval

Abstract IR revolves around evaluation. Therefore, IR researchers should employ sound evaluation practices. Nowadays many of us know that statistical significance testing is not enough, but not all of us know exactly what to do about it. This paper provides suggestions on how to report effect sizes and confidence intervals along with p-values, in the context of comparing IR systems using test collections. Hopefully, these practices will make IR papers more informative, and help researchers form more reliable conclusions that "add up." Finally, I pose a specific question for the IR community: should IR journal editors and SIGIR PC chairs require (rather than encourage) reporting of effect sizes and confidence intervals