共查询到10条相似文献，搜索用时 265 毫秒
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［目的/意义］在对MNCS和百分位数两种指标机制进行阐述、对比和分析的基础上,对百分位数指标的计算框架进行改进,提出一种动态权重的百分位数指标用于学术影响力的评价。［方法/过程］以ESI学科为评价对象,分别选取同一研究实体下的不同学科和不同研究实体下的同一学科作为两个实例进行实证研究。［结果/结论］实证结果表明这种动态权重的百分位数指标与MNCS和百分位数指标相比更能展现评价对象学术影响力的细节。 相似文献
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In the field of scientometrics, impact indicators and ranking algorithms are frequently evaluated using unlabelled test data comprising relevant entities (e.g., papers, authors, or institutions) that are considered important. The rationale is that the higher some algorithm ranks these entities, the better its performance. To compute a performance score for an algorithm, an evaluation measure is required to translate the rank distribution of the relevant entities into a single-value performance score. Until recently, it was simply assumed that taking the average rank (of the relevant entities) is an appropriate evaluation measure when comparing ranking algorithms or fine-tuning algorithm parameters.With this paper we propose a framework for evaluating the evaluation measures themselves. Using this framework the following questions can now be answered: (1) which evaluation measure should be chosen for an experiment, and (2) given an evaluation measure and corresponding performance scores for the algorithms under investigation, how significant are the observed performance differences?Using two publication databases and four test data sets we demonstrate the functionality of the framework and analyse the stability and discriminative power of the most common information retrieval evaluation measures. We find that there is no clear winner and that the performance of the evaluation measures is highly dependent on the underlying data. Our results show that the average rank is indeed an adequate and stable measure. However, we also show that relatively large performance differences are required to confidently determine if one ranking algorithm is significantly superior to another. Lastly, we list alternative measures that also yield stable results and highlight measures that should not be used in this context. 相似文献
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Fusion Via a Linear Combination of Scores 总被引：9，自引：2，他引：7
We present a thorough analysis of the capabilities of the linear combination (LC) model for fusion of information retrieval systems. The LC model combines the results lists of multiple IR systems by scoring each document using a weighted sum of the scores from each of the component systems. We first present both empirical and analytical justification for the hypotheses that such a model should only be used when the systems involved have high performance, a large overlap of relevant documents, and a small overlap of nonrelevant documents. The empirical approach allows us to very accurately predict the performance of a combined system. We also derive a formula for a theoretically optimal weighting scheme for combining 2 systems. We introduce d—the difference between the average score on relevant documents and the average score on nonrelevant documents—as a performance measure which not only allows mathematical reasoning about system performance, but also allows the selection of weights which generalize well to new documents. We describe a number of experiments involving large numbers of different IR systems which support these findings. 相似文献
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Robin Aly Aiden Doherty Djoerd Hiemstra Franciska de Jong Alan F. Smeaton 《Information Retrieval》2013,16(5):557-583
Concept based video retrieval often relies on imperfect and uncertain concept detectors. We propose a general ranking framework to define effective and robust ranking functions, through explicitly addressing detector uncertainty. It can cope with multiple concept-based representations per video segment and it allows the re-use of effective text retrieval functions which are defined on similar representations. The final ranking status value is a weighted combination of two components: the expected score of the possible scores, which represents the risk-neutral choice, and the scores’ standard deviation, which represents the risk or opportunity that the score for the actual representation is higher. The framework consistently improves the search performance in the shot retrieval task and the segment retrieval task over several baselines in five TRECVid collections and two collections which use simulated detectors of varying performance. 相似文献
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基于内容的图像检索技术是对图像的物理内容为加工对象的检索技术之一，主要实现方式包括基于颜色、纹理、形状、空间位置和语义等。其中基于颜色的图像检索发展最为成熟，而基于语义的检索则尚处于探讨、研究阶段。基于内容检索和基于文本检索在数字图书馆中可以融合共同提供检索服务。Google为这一尝试提供了在后控阶段的有效案例，而真正的实现两者的融合是在预处理阶段。两者结合在数字图书馆中的应用是可行的，相信能够提供更好的图像检索服务。 相似文献
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Knowledge transfer for cross domain learning to rank 总被引：1，自引：1，他引：0
Depin Chen Yan Xiong Jun Yan Gui-Rong Xue Gang Wang Zheng Chen 《Information Retrieval》2010,13(3):236-253
Recently, learning to rank technology is attracting increasing attention from both academia and industry in the areas of machine
learning and information retrieval. A number of algorithms have been proposed to rank documents according to the user-given
query using a human-labeled training dataset. A basic assumption behind general learning to rank algorithms is that the training
and test data are drawn from the same data distribution. However, this assumption does not always hold true in real world
applications. For example, it can be violated when the labeled training data become outdated or originally come from another
domain different from its counterpart of test data. Such situations bring a new problem, which we define as cross domain learning
to rank. In this paper, we aim at improving the learning of a ranking model in target domain by leveraging knowledge from
the outdated or out-of-domain data (both are referred to as source domain data). We first give a formal definition of the
cross domain learning to rank problem. Following this, two novel methods are proposed to conduct knowledge transfer at feature
level and instance level, respectively. These two methods both utilize Ranking SVM as the basic learner. In the experiments,
we evaluate these two methods using data from benchmark datasets for document retrieval. The results show that the feature-level
transfer method performs better with steady improvements over baseline approaches across different datasets, while the instance-level
transfer method comes out with varying performance depending on the dataset used. 相似文献
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[目的/意义]运用心流理论设计量表对信息检索体验进行测量与分析,探索个体在信息检索活动中的情感体验规律。[方法/过程]采用体验抽样法在三所大学图书馆对正在进行信息检索的用户进行问卷调查,利用数据统计分析检验量表的质量。依据调查对象在技巧维度、挑战维度及技巧与挑战平衡维度的得分,把样本划分为心流、焦虑、冷漠和无趣四通道,并比较不同通道的体验质量。[结果/结论]结果显示,该量表是测量信息检索体验的有效工具,技巧与挑战水平及两者的匹配程度是影响信息检索体验的关键变量,技巧与挑战匹配且都处于高水平的心流通道体验最佳,冷漠通道体验最差,焦虑和无趣通道居中。 相似文献