基于BP神经网络的共享服务平台资源信息质量评价研究——以短租类共享服务平台为例     

李全喜  徐嘉徽  魏骏巍  张健 《图书情报工作》2019,63(10):125-133

[目的/意义] 以短租类共享服务平台为例，构建共享服务平台资源信息质量评价指标体系，帮助此类平台企业高效地识别出存在信息质量问题的资源，提高平台整体的信息质量水平。[方法/过程] 首先基于信息传播学相关理论，对共享服务平台信息传播过程进行总结。然后根据共享服务平台信息传播的参与主体和访谈原始资料分析，构建共享服务平台资源信息质量评价指标体系，分为信源质量、信息内容质量和信息效用质量三个维度。最后提出基于BP神经网络的信息质量评价方法，并使用Matlab2018a软件对采集的100组样本数据进行训练和仿真验证。[结果/结论] 提出共享服务平台资源信息质量评价指标体系，并以短租类共享服务平台为例运用BP神经网络进行验证，实验证明该评价指标体系具有一定的可行性和实用性。  相似文献   

数据驱动的微信用户信息行为时间特征研究     

张大勇  孔洪新  许磊  景东 《图书情报工作》2019,63(20):104-111

[目的/意义]相对于传统的信息行为分析，数据驱动的信息行为研究更注重数据的外在性与客观性，所得的结果能够更为全面地认识用户信息行为本质特征。[方法/过程]通过自行构建的APP实现对微信用户分享和阅读行为记录的采集，并对微信用户信息行为的时间特性进行系统的分析。[结果/结论]结果表明：微信用户日常信息行为存在显著的假日效应，但是在信息行为时间间隔分布上存在明显厚尾现象和很强的阵发性，预示着微信用户信息行为具有较高的复杂性和不确定性，无法对其产生过程实现有效的预测；此外，微信用户所分享的内容具有很强的时效性，多数内容在微信中能够得到及时的传播，但传播链长度显著受分享内容主题的影响。  相似文献   

Online visibility of software-related web sites: The case of biomedical text mining tools     

Gael Pérez-Rodríguez  Martín Pérez-Pérez  Florentino Fdez-Riverola  Anália Lourenço 《Information processing & management》2019,56(3):565-583

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基于社会资本理论的社交问答用户健康信息行为研究     

陆泉  刘婷  邓胜利 《图书情报工作》2019,63(17):118-127

[目的/意义]社交问答用户的社会资本受多种因素影响，本文旨在探究社交问答用户不同的健康信息行为对其社会资本积累的影响。[方法/过程]以知乎网上糖尿病话题下2 537个问题帖子，3 567个回答的1 650名用户为研究对象，依据L.Nan的社会资本理论和N.Uphoff对社会资本的分类，将社交问答用户的社会资本分为认知性和结构性两类，用多元线性回归的方法分析社会问答用户的健康信息行为与社会资本之间的关系。[结果/结论]用户的健康知识贡献行为和自我信息披露行为在不同程度上正向促进社会资本的累积，而不同的健康知识获取行为对认知性社会资本和结构性社会资本的影响有差异。这些结果有助于社交问答用户提高社会资本，平台完善用户服务和激励机制。  相似文献   

Spatial Planning and its Need for National and Regional Bibliographies of Grey Literature     

Christian Erlinger 《Cataloging & classification quarterly》2019,57(1):59-72

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Engagement with news on Twitter: insights from Australia and Korea     

Robert Ackland  Mathieu O’Neil  Sora Park 《亚洲交流杂志》2019,29(3):235-251

ABSTRACT

This study examines the patterns of news engagement among news consumers with different political affiliation and cultural background. We use computational methods and data from Twitter in a cross-country comparison of engagement with six online news sources in Australia and South Korea. For our analysis, we used a subset of Twitter users who retweeted at least one political story during the period of collection, and for whom we were able to predict political affiliation using correspondence analysis and data on Twitter follower ties to politicians. We find that right-wing Australian retweeters are more intense in their news engagement, compared with their left-wing counterparts, whereas in South Korea it was the opposite. Australian right-wing political retweeters have more diverse information sources, while there was no difference in information diversity between the right and left in South Korea. We discuss how the political situation in South Korea at the time of data collection may have affected our analysis. We emphasise the methodological contributions of our research and its connection to on-going research into the behavioural foundations of ‘filter bubbles’.  相似文献   

图书馆情报学主要研究方法:了解、选择及使用     

储荷婷 《图书情报工作》2019,63(1):146-152

[目的/意义]研究方法在学术研究中发挥着至关重要的作用。确认图书馆情报学领域主要的研究方法，并对它们进行了解熟悉，以在开展研究时能合理选择、灵活使用，确保研究质量。[方法/过程]对近2 000篇图书馆情报学领域的研究文献以及相关研究方法论文进行内容分析，在此基础上对研究方法的类分命名、图书馆情报学界主要的研究方法的确定、特点和使用注意事项进行介绍和讨论。[结果/结论]研究方法应以数据收集法而不是数据分析法命名。图书馆情报学领域常用的研究方法包括实验法、问卷法、理论研讨法、内容分析法、访谈法和书目计量法，每种方法都有各自的特点。因而在选择使用时，既应根据具体研究课题及研究方法之特性，也要考虑使用注意事项，并尽量在同一研究中采用两种或更多的方法，以扬长避短，更有效地展开研究。  相似文献   

Web document summarization by exploiting social context with matrix co-factorization     

Minh-Tien Nguyen  Viet Cuong Tran  Xuan Hoai Nguyen  Le-Minh Nguyen 《Information processing & management》2019,56(3):495-515

In the context of social media, users usually post relevant information corresponding to the contents of events mentioned in a Web document. This information posses two important values in that (i) it reflects the content of an event and (ii) it shares hidden topics with sentences in the main document. In this paper, we present a novel model to capture the nature of relationships between document sentences and post information (comments or tweets) in sharing hidden topics for summarization of Web documents by utilizing relevant post information. Unlike previous methods which are usually based on hand-crafted features, our approach ranks document sentences and user posts based on their importance to the topics. The sentence-user-post relation is formulated in a share topic matrix, which presents their mutual reinforcement support. Our proposed matrix co-factorization algorithm computes the score of each document sentence and user post and extracts the top ranked document sentences and comments (or tweets) as a summary. We apply the model to the task of summarization on three datasets in two languages, English and Vietnamese, of social context summarization and also on DUC 2004 (a standard corpus of the traditional summarization task). According to the experimental results, our model significantly outperforms the basic matrix factorization and achieves competitive ROUGE-scores with state-of-the-art methods.  相似文献   

Neural embedding-based indices for semantic search     

Fatemeh Lashkari  Ebrahim Bagheri  Ali A. Ghorbani 《Information processing & management》2019,56(3):733-755

Traditional information retrieval techniques that primarily rely on keyword-based linking of the query and document spaces face challenges such as the vocabulary mismatch problem where relevant documents to a given query might not be retrieved simply due to the use of different terminology for describing the same concepts. As such, semantic search techniques aim to address such limitations of keyword-based retrieval models by incorporating semantic information from standard knowledge bases such as Freebase and DBpedia. The literature has already shown that while the sole consideration of semantic information might not lead to improved retrieval performance over keyword-based search, their consideration enables the retrieval of a set of relevant documents that cannot be retrieved by keyword-based methods. As such, building indices that store and provide access to semantic information during the retrieval process is important. While the process for building and querying keyword-based indices is quite well understood, the incorporation of semantic information within search indices is still an open challenge. Existing work have proposed to build one unified index encompassing both textual and semantic information or to build separate yet integrated indices for each information type but they face limitations such as increased query process time. In this paper, we propose to use neural embeddings-based representations of term, semantic entity, semantic type and documents within the same embedding space to facilitate the development of a unified search index that would consist of these four information types. We perform experiments on standard and widely used document collections including Clueweb09-B and Robust04 to evaluate our proposed indexing strategy from both effectiveness and efficiency perspectives. Based on our experiments, we find that when neural embeddings are used to build inverted indices; hence relaxing the requirement to explicitly observe the posting list key in the indexed document: (a) retrieval efficiency will increase compared to a standard inverted index, hence reduces the index size and query processing time, and (b) while retrieval efficiency, which is the main objective of an efficient indexing mechanism improves using our proposed method, retrieval effectiveness also retains competitive performance compared to the baseline in terms of retrieving a reasonable number of relevant documents from the indexed corpus.  相似文献   

网络招聘文本技能信息自动抽取研究   总被引：1，自引：1，他引：0  

俞琰  陈磊  姜金德  赵乃瑄 《图书情报工作》2019,63(13):105-113

[目的/意义]针对目前网络招聘文本手工抽取技能信息无法满足大数据量分析要求的问题，提出一种针对大量网络招聘文本的技能信息自动抽取方法。[方法/过程]根据网络招聘文本的特点，利用依存句法分析选取候选技能，然后提出领域相关性指标衡量候选技能，将其融入传统的术语抽取方法之中，形成一种网络招聘文本技能信息自动抽取方法。[结果/结论]实验表明，本文提出的方法能够从网络招聘文本中自动、快速、准确地抽取技能信息。  相似文献