In recent years, the proliferation of smart mobile devices has lead to the gradual integration of search functionality within mobile platforms. This has created an incentive to move away from the "ten blue links" metaphor, as mobile users are less likely to click on them, expecting to get the answer directly from the snippets. In turn, this has revived the interest in Question Answering. Then, along came chatbots, conversational systems, and messaging platforms, where the user needs could be better served with the system asking follow-up questions in order to better understand the user's intent. While typically a user would expect a single response at any utterance, a system could also return multiple options for the user to select from, based on different system understandings of the user's intent. However, this possibility should not be overused, as this practice could confuse and or annoy the user. How to produce good variable-length lists, given the conflicting objectives of staying short while maximizing the likelihood of having a correct answer included in the list, is an underexplored problem. It is also unclear how to evaluate a system that tries to do that. Here we aim to bridge this gap. In particular, we define some necessary and some optional properties that an evaluation measure fit for this purpose should have. We further show that existing evaluation measures from the IR tradition are not entirely suitable for this setup, and we propose novel evaluation measures that address it satisfactorily.
@cite_19 defined seven properties for effectiveness measures. The relevant properties, which our new evaluation measures also satisfy are , , and . However, their property contradicts our property because it states that adding documents, which are not relevant, to the end of the list increases the score.
@cite_19
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Search effectiveness metrics quantify the relevance of the ranked document lists returned by retrieval systems. In this paper we characterize metrics according to seven numeric properties – boundedness, monotonicity, convergence, top-weightedness, localization, completeness, and realizability. We demonstrate that these properties partition the commonly-used evaluation metrics, and hence provide a framework in which the relationships between effectiveness metrics can be better understood, including their relative merits for different applications. In this paper we present some new methods of ranking information retrieval systems without relevance judgement. The common ground of these methods is using a measure we called reference count. An extensive experimentation was conducted to evaluate the effectiveness of the proposed methods using various different standards Information Retrieval evaluation measures for the ranking, like average precision, R-precision, and precision and different document levels. We also compared the effectiveness of the proposed methods with the method proposed by The experimental results showed that the proposed methods are effective, and in many cases are more effective than Soboroff at al.'s method. A wide range of evaluation metrics have been proposed to measure the quality of search results, including in the presence of diversification. Some of these metrics have been adapted for use in search tasks with different complexities, such as where the search system returns lists of different lengths. Given the range of requirements, it can be difficult to compare the behavior of these metrics. In this work, we examine effectiveness metrics using a simple property-based approach. In particular, we present a case-analysis framework to define and study fundamental properties that seem integral to any evaluation metric. An example of a simple property is that a ranking with only one non-relevant document should never score lower than a ranking with two non-relevant documents. The framework facilitates quantifying the ability of metrics to satisfy properties, both separately and simultaneously, and to identify those cases where properties are violated. Our analysis shows that the Average Cube Test and Intent-Aware Average Precision are two metrics which fail to satisfy the desirable properties, and hence should be used with caution. Modern large retrieval environments tend to overwhelm their users by their large output. Since all documents are not of equal relevance to their users, highly relevant documents should be identified and ranked first for presentation. In order to develop IR techniques in this direction, it is necessary to develop evaluation approaches and methods that credit IR methods for their ability to retrieve highly relevant documents. This can be done by extending traditional evaluation methods, that is, recall and precision based on binary relevance judgments, to graded relevance judgments. Alternatively, novel measures based on graded relevance judgments may be developed. This article proposes several novel measures that compute the cumulative gain the user obtains by examining the retrieval result up to a given ranked position. The first one accumulates the relevance scores of retrieved documents along the ranked result list. The second one is similar but applies a discount factor to the relevance scores in order to devaluate late-retrieved documents. The third one computes the relative-to-the-ideal performance of IR techniques, based on the cumulative gain they are able to yield. These novel measures are defined and discussed and their use is demonstrated in a case study using TREC data: sample system run results for 20 queries in TREC-7. As a relevance base we used novel graded relevance judgments on a four-point scale. The test results indicate that the proposed measures credit IR methods for their ability to retrieve highly relevant documents and allow testing of statistical significance of effectiveness differences. The graphs based on the measures also provide insight into the performance IR techniques and allow interpretation, for example, from the user point of view.