Because researchers typically do not have the time or space to present more than a few evaluation metrics in any published study, it can be difficult to assess relative effectiveness of prior methods for unreported metrics when baselining a new method or conducting a systematic meta-review. While sharing of study data would help alleviate this, recent attempts to encourage consistent sharing have been largely unsuccessful. Instead, we propose to enable relative comparisons with prior work across arbitrary metrics by predicting unreported metrics given one or more reported metrics. In addition, we further investigate prediction of high-cost evaluation measures using low-cost measures as a potential strategy for reducing evaluation cost. We begin by assessing the correlation between 23 IR metrics using 8 TREC test collections. Measuring prediction error wrt. R-square and Kendall's tau, we show that accurate prediction of MAP, P@10, and RBP can be achieved using only 2-3 other metrics. With regard to lowering evaluation cost, we show that RBP(p=0.95) can be predicted with high accuracy using measures with only evaluation depth of 30. Taken together, our findings provide a valuable proof-of-concept which we expect to spur follow-on work by others in proposing more sophisticated models for metric prediction.
A primary contribution of our work is investigating prediction of evaluation measures. While @cite_12 also proposes predicting evaluation measures, they require a corresponding retrieved ranked list as well as another evaluation metric. They conclude that they can infer accurately user-oriented measures (e.g. P@10) from system-oriented measures (e.g. AP, R-Prec). In contrast, we predict evaluation measure of a system given only other evaluation measures without requiring the corresponding ranked lists.
@cite_12
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We introduce a general information access evaluation framework that can potentially handle summaries, ranked document lists and even multi query sessions seamlessly. Our framework first builds a trailtext which represents a concatenation of all the texts read by the user during a search session, and then computes an evaluation metric called U-measure over the trailtext. Instead of discounting the value of a retrieved piece of information based on ranks, U-measure discounts it based on its position within the trailtext. U-measure takes the document length into account just like Time-Biased Gain (TBG), and has the diminishing return property. It is therefore more realistic than rank-based metrics. Furthermore, it is arguably more flexible than TBG, as it is free from the linear traversal assumption (i.e., that the user scans the ranked list from top to bottom), and can handle information access tasks other than ad hoc retrieval. This paper demonstrates the validity and versatility of the U-measure framework. Our main conclusions are: (a) For ad hoc retrieval, U-measure is at least as reliable as TBG in terms of rank correlations with traditional metrics and discriminative power; (b) For diversified search, our diversity versions of U-measure are highly correlated with state-of-the-art diversity metrics; (c) For multi-query sessions, U-measure is highly correlated with Session nDCG; and (d) Unlike rank-based metrics such as DCG, U-measure can quantify the differences between linear and nonlinear traversals in sessions. We argue that our new framework is useful for understanding the user's search behaviour and for comparison across different information access styles (e.g. examining a direct answer vs. examining a ranked list of web pages). Search queries are often ambiguous and or underspecified. To accomodate different user needs, search result diversification has received attention in the past few years. Accordingly, several new metrics for evaluating diversification have been proposed, but their properties are little understood. We compare the properties of existing metrics given the premises that (1) queries may have multiple intents; (2) the likelihood of each intent given a query is available; and (3) graded relevance assessments are available for each intent. We compare a wide range of traditional and diversified IR metrics after adding graded relevance assessments to the TREC 2009 Web track diversity task test collection which originally had binary relevance assessments. Our primary criterion is discriminative power, which represents the reliability of a metric in an experiment. Our results show that diversified IR experiments with a given number of topics can be as reliable as traditional IR experiments with the same number of topics, provided that the right metrics are used. Moreover, we compare the intuitiveness of diversified IR metrics by closely examining the actual ranked lists from TREC. We show that a family of metrics called D#-measures have several advantages over other metrics such as α-nDCG and Intent-Aware metrics. The most prevalent experimental methodology for comparing the effectiveness of information retrieval systems requires a test collection, composed of a set of documents, a set of query topics, and a set of relevance judgments indicating which documents are relevant to which topics. It is well known that relevance judgments are not infallible, but recent retrospective investigation into results from the Text REtrieval Conference (TREC) has shown that differences in human judgments of relevance do not affect the relative measured performance of retrieval systems. Based on this result, we propose and describe the initial results of a new evaluation methodology which replaces human relevance judgments with a randomly selected mapping of documents to topics which we refer to as pseudo-relevance judgments .Rankings of systems with our methodology correlate positively with official TREC rankings, although the performance of the top systems is not predicted well. The correlations are stable over a variety of pool depths and sampling techniques. With improvements, such a methodology could be useful in evaluating systems such as World-Wide Web search engines, where the set of documents changes too often to make traditional collection construction techniques practical. Relevance judgments are often the most expensive part of information retrieval evaluation, and techniques for comparing retrieval systems using fewer relevance judgments have received significant attention in recent years. This paper proposes a novel system comparison method using an expectation-maximization algorithm. In the expectation step, real-valued pseudo-judgments are estimated from a set of system results. In the maximization step, new system weights are learned from a combination of a limited number of actual human judgments and system pseudo-judgments for the other documents. The method can work without any human judgments, and is able to improve its accuracy by incrementally adding human judgments. Experiments using TREC Ad Hoc collections demonstrate strong correlations with system rankings using pooled human judgments, and comparison with existing baselines indicates that the new method achieves the same comparison reliability with fewer human judgments.