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1.
《Journal of Informetrics》2022,16(2):101283
Choosing a publication venue for an academic paper is a crucial step in the research process. However, in many cases, decisions are based solely on the experience of researchers, which often leads to suboptimal results. Although there exist venue recommender systems for academic papers, they recommend venues where the paper is expected to be published. In this study, we aim to recommend publication venues from a different perspective. We estimate the number of citations a paper will receive if the paper is published in each venue and recommend the venue where the paper has the most potential impact. However, there are two challenges to this task. First, a paper is published in only one venue, and thus, we cannot observe the number of citations the paper would receive if the paper were published in another venue. Secondly, the contents of a paper and the publication venue are not statistically independent; that is, there exist selection biases in choosing publication venues. In this paper, we formulate the venue recommendation problem as a treatment effect estimation problem. We use a bias correction method to estimate the potential impact of choosing a publication venue effectively and to recommend venues based on the potential impact of papers in each venue. We highlight the effectiveness of our method using paper data from computer science conferences. 相似文献
2.
There is an increasing consensus in the Recommender Systems community that the dominant error-based evaluation metrics are insufficient, and mostly inadequate, to properly assess the practical effectiveness of recommendations. Seeking to evaluate recommendation rankings—which largely determine the effective accuracy in matching user needs—rather than predicted rating values, Information Retrieval metrics have started to be applied for the evaluation of recommender systems. In this paper we analyse the main issues and potential divergences in the application of Information Retrieval methodologies to recommender system evaluation, and provide a systematic characterisation of experimental design alternatives for this adaptation. We lay out an experimental configuration framework upon which we identify and analyse specific statistical biases arising in the adaptation of Information Retrieval metrics to recommendation tasks, namely sparsity and popularity biases. These biases considerably distort the empirical measurements, hindering the interpretation and comparison of results across experiments. We develop a formal characterisation and analysis of the biases upon which we analyse their causes and main factors, as well as their impact on evaluation metrics under different experimental configurations, illustrating the theoretical findings with empirical evidence. We propose two experimental design approaches that effectively neutralise such biases to a large extent. We report experiments validating our proposed experimental variants, and comparing them to alternative approaches and metrics that have been defined in the literature with similar or related purposes. 相似文献
3.
《Journal of Informetrics》2020,14(2):101035
Performance evaluation and prediction of academic achievements is an essential task for scientists, research organizations, research funding bodies, and government agencies alike. Recently, heterogeneous networks have been used to evaluate or predict performance of multi-entities including papers, researchers, and venues with some success. However, only a minimum of effort has been made to predict the future influence of papers, researchers and venues. In this paper, we propose a new framework WMR-Rank for this purpose. Based on the dynamic and heterogeneous network of multiple entities, we extract seven types of relations among them. The framework supports useful features including the refined granularity of relevant entities such as authors and venues, time awareness for published papers and their citations, differentiating the contribution of multiple coauthors to the same paper, amongst others. By leveraging all seven types of relations and fusing the rich information in a mutually reinforcing style, we are able to predict future influence of papers, authors and venues more precisely. Using the ACL dataset, our experimental results demonstrate that the proposed approach considerably outperforms state-of-the art competitors. 相似文献
4.
《Journalism Practice》2013,7(10):1220-1240
In recent years, the rapid expansion of Web 2.0 tools has opened new possibilities for audience participation in news, while “engagement” has become a media industry buzzword. In this study, we explore approaches to engagement emerging in the field based on in-depth interviews with editors at a range of news outlets from several countries, and we map these approaches onto the literature on participatory journalism and related innovations in journalism practice. Our findings suggest variation in approaches to engagement that can be arrayed along several related dimensions, encompassing how news outlets measure and practice it (e.g. with the use of quantitative audience metrics methods), whether they think about audiences as more passive or more active users, the stages at which they incorporate audience data or input into the news product, and how skeptically or optimistically they view the audience. Overall, while some outlets are experimenting with tools for more substantive audience contributions to news content, we find few outlets approaching engagement as a way to involve users in the creation of news, with most in our sample focusing mostly on engaging users in back-end reaction and response to the outlet’s content. We identify technological, economic, professional, and organizational factors that shape and constrain how news outlets practice “engagement.” 相似文献
5.
科技馆作为重要的科普基础设施,在科普工作中发挥着重要作用。本文以全国科普统计调查的定量数据统计分析为主,对国内科技馆的总体情况、存在问题和未来需要关注的一些方向进行分析。可以看出,在财政经费资助下,国内科技馆数量快速增长,特别是一些特大型场馆相继开放,在科普工作中发挥了巨大作用。但也存在场馆分布不均、资源配套落后、科普功能发挥不足等问题。在未来的工作中,需要注意场馆建设与地区发展同步,以及场馆布局的公平性、可达性等问题。 相似文献
6.
A number of online marketplaces enable customers to buy or sell used products, which raises the need for ranking tools to help them find desirable items among a huge pool of choices. To the best of our knowledge, no prior work in the literature has investigated the task of used product ranking which has its unique characteristics compared with regular product ranking. While there exist a few ranking metrics (e.g., price, conversion probability) that measure the “goodness” of a product, they do not consider the time factor, which is crucial in used product trading due to the fact that each used product is often unique while new products are usually abundant in supply or quantity. In this paper, we introduce a novel time-aware metric—“sellability”, which is defined as the time duration for a used item to be traded, to quantify the value of it. In order to estimate the “sellability” values for newly generated used products and to present users with a ranked list of the most relevant results, we propose a combined Poisson regression and listwise ranking model. The model has a good property in fitting the distribution of “sellability”. In addition, the model is designed to optimize loss functions for regression and ranking simultaneously, which is different from previous approaches that are conventionally learned with a single cost function, i.e., regression or ranking. We evaluate our approach in the domain of used vehicles. Experimental results show that the proposed model can improve both regression and ranking performance compared with non-machine learning and machine learning baselines. 相似文献
7.
Large-scale retrieval systems are often implemented as a cascading sequence of phases—a first filtering step, in which a large set of candidate documents are extracted using a simple technique such as Boolean matching and/or static document scores; and then one or more ranking steps, in which the pool of documents retrieved by the filter is scored more precisely using dozens or perhaps hundreds of different features. The documents returned to the user are then taken from the head of the final ranked list. Here we examine methods for measuring the quality of filtering and preliminary ranking stages, and show how to use these measurements to tune the overall performance of the system. Standard top-weighted metrics used for overall system evaluation are not appropriate for assessing filtering stages, since the output is a set of documents, rather than an ordered sequence of documents. Instead, we use an approach in which a quality score is computed based on the discrepancy between filtered and full evaluation. Unlike previous approaches, our methods do not require relevance judgments, and thus can be used with virtually any query set. We show that this quality score directly correlates with actual differences in measured effectiveness when relevance judgments are available. Since the quality score does not require relevance judgments, it can be used to identify queries that perform particularly poorly for a given filter. Using these methods, we explore a wide range of filtering options using thousands of queries, categorize the relative merits of the different approaches, and identify useful parameter combinations. 相似文献
8.
The increasing trend of cross-border globalization and acculturation requires text summarization techniques to work equally well for multiple languages. However, only some of the automated summarization methods can be defined as “language-independent,” i.e., not based on any language-specific knowledge. Such methods can be used for multilingual summarization, defined in Mani (Automatic summarization. Natural language processing. John Benjamins Publishing Company, Amsterdam, 2001) as “processing several languages, with a summary in the same language as input”, but, their performance is usually unsatisfactory due to the exclusion of language-specific knowledge. Moreover, supervised machine learning approaches need training corpora in multiple languages that are usually unavailable for rare languages, and their creation is a very expensive and labor-intensive process. In this article, we describe cross-lingual methods for training an extractive single-document text summarizer called MUSE (MUltilingual Sentence Extractor)—a supervised approach, based on the linear optimization of a rich set of sentence ranking measures using a Genetic Algorithm. We evaluated MUSE’s performance on documents in three different languages: English, Hebrew, and Arabic using several training scenarios. The summarization quality was measured using ROUGE-1 and ROUGE-2 Recall metrics. The results of the extensive comparative analysis showed that the performance of MUSE was better than that of the best known multilingual approach (TextRank) in all three languages. Moreover, our experimental results suggest that using the same sentence ranking model across languages results in a reasonable summarization quality, while saving considerable annotation efforts for the end-user. On the other hand, using parallel corpora generated by machine translation tools may improve the performance of a MUSE model trained on a foreign language. Comparative evaluation of an alternative optimization technique—Multiple Linear Regression—justifies the use of a Genetic Algorithm. 相似文献
9.
《Journal of Informetrics》2019,13(2):485-499
With the growing number of published scientific papers world-wide, the need to evaluation and quality assessment methods for research papers is increasing. Scientific fields such as scientometrics, informetrics, and bibliometrics establish quantified analysis methods and measurements for evaluating scientific papers. In this area, an important problem is to predict the future influence of a published paper. Particularly, early discrimination between influential papers and insignificant papers may find important applications. In this regard, one of the most important metrics is the number of citations to the paper, since this metric is widely utilized in the evaluation of scientific publications and moreover, it serves as the basis for many other metrics such as h-index. In this paper, we propose a novel method for predicting long-term citations of a paper based on the number of its citations in the first few years after publication. In order to train a citation count prediction model, we employed artificial neural network which is a powerful machine learning tool with recently growing applications in many domains including image and text processing. The empirical experiments show that our proposed method outperforms state-of-the-art methods with respect to the prediction accuracy in both yearly and total prediction of the number of citations. 相似文献
10.
11.
Although the 542 pages of the book suggest that this is yourtypical reader with many contributors, all its chapters arewritten by only four scholars. They are the collaborators ofa joint research project at the universities of 相似文献
12.
This study introduces a novel framework for evaluating passage and XML retrieval. The framework focuses on a user’s effort
to localize relevant content in a result document. Measuring the effort is based on a system guided reading order of documents.
The effort is calculated as the quantity of text the user is expected to browse through. More specifically, this study seeks
evaluation metrics for retrieval methods following a specific fetch and browse approach, where in the fetch phase documents
are ranked in decreasing order according to their document score, like in document retrieval. In the browse phase, for each
retrieved document, a set of non-overlapping passages representing the relevant text within the document is retrieved. In
other words, the passages of the document are re-organized, so that the best matching passages are read first in sequential
order. We introduce an application scenario motivating the framework, and propose sample metrics based on the framework. These
metrics give a basis for the comparison of effectiveness between traditional document retrieval and passage/XML retrieval
and illuminate the benefit of passage/XML retrieval. 相似文献
13.
Recently, two new indicators (Equalized Mean-based Normalized Proportion Cited, EMNPC; Mean-based Normalized Proportion Cited, MNPC) were proposed which are intended for sparse scientometrics data, e.g., alternative metrics (altmetrics). The indicators compare the proportion of mentioned papers (e.g. on Facebook) of a unit (e.g., a researcher or institution) with the proportion of mentioned papers in the corresponding fields and publication years (the expected values). In this study, we propose a third indicator (Mantel-Haenszel quotient, MHq) belonging to the same indicator family. The MHq is based on the MH analysis – an established method in statistics for the comparison of proportions. We test (using citations and assessments by peers, i.e. F1000Prime recommendations) if the three indicators can distinguish between different quality levels as defined on the basis of the assessments by peers. Thus, we test their convergent validity. We find that the indicator MHq is able to distinguish between the quality levels in most cases while MNPC and EMNPC are not. Since the MHq is shown in this study to be a valid indicator, we apply it to six types of zero-inflated altmetrics data and test whether different altmetrics sources are related to quality. The results for the various altmetrics demonstrate that the relationship between altmetrics (Wikipedia, Facebook, blogs, and news data) and assessments by peers is not as strong as the relationship between citations and assessments by peers. Actually, the relationship between citations and peer assessments is about two to three times stronger than the association between altmetrics and assessments by peers. 相似文献
14.
《Communication methods and measures》2013,7(2):116-137
Communication research based on social cognition draws heavily from dual process models such as the heuristic-systematic model. Both heuristic and systematic processing are said to be aided by quick rules of thumb or mental shortcuts called heuristics. However, the operationalization of heuristics is quite problematic because their use in decision-making is not directly measured. Scholars claim the operation of specific heuristics in specific situations, based often on clever experimental evidence. We propose a methodological framework that calls for both manipulation and measurement of heuristics in order to offer more direct evidence of their operation. We first review different existing approaches in the literature for operationalizing heuristics. We then discuss our approach and describe the application of moderated mediation to analyze the resulting data. We conclude with a study idea and simulated data that illustrate how our proposed design and analysis framework could be applied in communication research. 相似文献
15.
[目的/意义]从民众信息需求与信息服务的视角提出政府信息公开效果的评价方法,丰富情报学在政府信息评价中的应用研究。[方法/过程]选取教育、住房、医疗等12个民生相关领域的生活事件,通过不同角度的查询语句模拟人们对上述事件的信息需求。定义匹配率、可见度、覆盖率以及查询平均有效网页数等指标来衡量政府网站公开的信息符合人们需求的情况。结合检索系统和人工筛选共同判断哪些网页符合人们的信息需求。采用全国35个城市政府网站的227万网页数据进行评价实验,评价指标兼顾对内容层面的相关性和评价可操作性的考虑。[结果/结论]本文将信息公开视为政府向民众提供的一种信息服务,根据公开内容所能满足的民众信息需求定义评价指标,衡量信息公开的实际效果。并通过对35个城市政府网站进行不同维度的指标计算得到评价结果,检验了该方法在政府信息公开实际效果评价上的可操作性。 相似文献
16.
An Empirical Analysis of Design Choices in Neighborhood-Based Collaborative Filtering Algorithms 总被引：4，自引：0，他引：4
Collaborative filtering systems predict a user's interest in new items based on the recommendations of other people with similar interests. Instead of performing content indexing or content analysis, collaborative filtering systems rely entirely on interest ratings from members of a participating community. Since predictions are based on human ratings, collaborative filtering systems have the potential to provide filtering based on complex attributes, such as quality, taste, or aesthetics. Many implementations of collaborative filtering apply some variation of the neighborhood-based prediction algorithm. Many variations of similarity metrics, weighting approaches, combination measures, and rating normalization have appeared in each implementation. For these parameters and others, there is no consensus as to which choice of technique is most appropriate for what situations, nor how significant an effect on accuracy each parameter has. Consequently, every person implementing a collaborative filtering system must make hard design choices with little guidance. This article provides a set of recommendations to guide design of neighborhood-based prediction systems, based on the results of an empirical study. We apply an analysis framework that divides the neighborhood-based prediction approach into three components and then examines variants of the key parameters in each component. The three components identified are similarity computation, neighbor selection, and rating combination. 相似文献
17.
《Journal of Informetrics》2014,8(1):217-233
Research institutions play an important role in scientific research and technical innovation. The topical analysis of research institutions in different countries can facilitate mutual learning and promote potential collaboration. In this study, we illustrate how an unsupervised artificial neural network technique Self-Organizing Map (SOM) can be used to visually analyze the research fields of research institutions. A novel SOM display named Compound Component Plane (CCP) was presented and applied to determine the institutions which made significant contributions to the salient research fields. Eighty-seven Chinese and American LIS institutions and the technical LIS fields were taken as examples. Potential international and domestic collaborators were identified based upon their research similarities. An approach of dividing research institutions into clusters was proposed based on their geometric distances in the SOM display, the U-matrix values and the most salient research topics they involved. The concepts of swarm institutions, pivots and landmarks were also defined and their instances were identified. 相似文献
18.
近年来,随着信息技术的高速发展和科技馆管理要求的不断提高,为更好地了解和掌握观众所需,提升科技馆的管理和服务能力,采用大数据等先进的信息技术手段,创新更加人性化的贴身服务,已成为科技馆的迫切需求。本文以中国科技馆观众大数据分析平台为例,介绍了如何通过移动终端信号监测解决场馆内精准定位问题,并运用运营商大数据实现对科技馆客流统计、观众画像和行为分析,为大数据在科普场馆智慧化应用方面提供参考。 相似文献
19.
We analyse the difference between the averaged (average of ratios) and globalised (ratio of averages) author-level aggregation approaches based on various paper-level metrics. We evaluate the aggregation variants in terms of (1) their field bias on the author-level and (2) their ranking performance based on test data that comprises researchers that have received fellowship status or won prestigious awards for their long-lasting and high-impact research contributions to their fields. We consider various direct and indirect paper-level metrics with different normalisation approaches (mean-based, percentile-based, co-citation-based) and focus on the bias and performance differences between the two aggregation variants of each metric. We execute all experiments on two publication databases which use different field categorisation schemes. The first uses author-chosen concept categories and covers the computer science literature. The second covers all disciplines and categorises papers by keywords based on their contents. In terms of bias, we find relatively little difference between the averaged and globalised variants. For mean-normalised citation counts we find no significant difference between the two approaches. However, the percentile-based metric shows less bias with the globalised approach, except for citation windows smaller than four years. On the multi-disciplinary database, PageRank has the overall least bias but shows no significant difference between the two aggregation variants. The averaged variants of most metrics have less bias for small citation windows. For larger citation windows the differences are smaller and are mostly insignificant.In terms of ranking the well-established researchers who have received accolades for their high-impact contributions, we find that the globalised variant of the percentile-based metric performs better. Again we find no significant differences between the globalised and averaged variants based on citation counts and PageRank scores. 相似文献
20.
A general model of performance and quality for one-stop e-Government service offerings 总被引：2，自引：2，他引：2
This paper introduces a framework and methodology for establishing indicators and metrics in order to assess the quality and performance of one-stop e-Government service offerings. The set of quality and performance indicators and metrics proposed has been derived in an outcomes assessment approach, based on the perspectives of e-Government service providers and end-users and following a goal-question-metric line of work that departs from some key quality and performance benefits. A methodology that employs the proposed framework to set improvement targets according to alternative scenarios is presented, and a strategy is elaborated for analyzing root causes of potential quality and performance shortcomings and undertaking appropriate countermeasures. Some results of application in a real case study, in the context of an EU-funded R&D project, are also provided. Finally, recommendations are given about usefulness of the proposed approach for e-Government service providers, as well as policy and decision-makers, and directions of future work are discussed in order to enhance the conceptual coverage of this approach, while at the same time not compromising its simplicity of application and generality of purpose. 相似文献