共查询到20条相似文献，搜索用时 203 毫秒
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本系统基于模糊联想记忆神经网络,建立偏好评价模型,根据用户偏好对搜索引擎搜索到的候选文献进行评级,为用户推荐偏好值高的文献。本系统的学习模块采用PCA-CG算法和误差反向传播算法,以用户阅读过的基准文献和其对应评级作为训练样本,对用户偏好进行学习;推理模块根据学习到的模糊规则和隶属函数来计算候选文献的偏好值,并以偏好值排序,把偏好值高的文献推荐给用户。把该模型应用于信息技术类文献的检索,实验表明系统提供的推荐文献具有较高可信度。 相似文献
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用户对文献的需求与图书馆文献资源建设之间日益尖锐的供求矛盾。使文献传递服务成了近几年的热门研究．笔者一直从事文献传递工作．因此，本文根据我馆文献传递服务实践涉及到的各个方面，构建我馆服务质量综合评价层次结构模型，对我馆开展的文献传递服务进行具体评析，以便规范我馆文献传递服务与创新发展。 相似文献
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[目的/意义]面向非相关文献的知识关联能够促进新知识的产生，为科学研究提供了一种有效的辅助手段。[方法/过程]本文以《中国分类主题词表》为主题词受控词表，首先对文献摘要进行中文分词处理并提取主题词，利用计量分析技术和聚类技术分析文献间特征的相似、相异水平，然后基于该系统为用户检索并利用用TOP-K算法反馈用户精确结果。[结果/结论]设计了面向非相关文献的知识关联检索系统，从更细的粒度层面揭示文献之间的知识关联，为用户提供高质量的服务。 相似文献
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文献传递服务是一个系统工程,必须运用系统科学的思维和方法来研究。采用系统论的基本原则,对文献传递服务的整体性、关联性、有序性和动态性等系统特性进行分析,提出通过整合文献传递系统内各要素、优化系统结构来提升文献传递服务质量和效率。 相似文献
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阐述科学文献中机构要素的含义,分析其属性特征,总结机构要素间呈现的关联模式,探讨构建机构要素词典的方法,并在此基础上构建了机构要素词典模型;以实例的方式展示机构要素词典的体系结构,最后说明其应用. 相似文献
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遴选出四种权威性的陶瓷期刊，在10年的跨度中，根据引文要素进行统计数据的动态分析，阐述了开发陶瓷文献应注意的各种问题及现实意义，总结开发的有效形式，以满足不同读者和用户的各种需求。 相似文献
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图书馆应根据各种不同层次用户的需求，利用多种形式对馆藏文献信息资源进行有针对性的开发，使开发工作适合用户的需求，有效地为用户服务。本文论述了图书馆文献信息资源的开发与用户需求之间的关系和文献信息资源开发的形式。 相似文献
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高校图书馆电子文献传递服务的创新与实践——以西北师范大学图书馆为例 总被引：1，自引：0，他引：1
文章通过分析当前高校图书馆电子文献传递服务发展现状与特点,指出高校馆文献传递工作存在的问题及不足,并以西北师范大学图书馆为例,介绍如何根据该馆文献传递服务的实际特点和读者需要,创新工作思路与方法,构建用户"零距离"的文献传递服务系统. 相似文献
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《Information processing & management》2020,57(3):102216
Learning semantic representations of documents is essential for various downstream applications, including text classification and information retrieval. Entities, as important sources of information, have been playing a crucial role in assisting latent representations of documents. In this work, we hypothesize that entities are not monolithic concepts; instead they have multiple aspects, and different documents may be discussing different aspects of a given entity. Given that, we argue that from an entity-centric point of view, a document related to multiple entities shall be (a) represented differently for different entities (multiple entity-centric representations), and (b) each entity-centric representation should reflect the specific aspects of the entity discussed in the document.In this work, we devise the following research questions: (1) Can we confirm that entities have multiple aspects, with different aspects reflected in different documents, (2) can we learn a representation of entity aspects from a collection of documents, and a representation of document based on the multiple entities and their aspects as reflected in the documents, (3) does this novel representation improves algorithm performance in downstream applications, and (4) what is a reasonable number of aspects per entity? To answer these questions we model each entity using multiple aspects (entity facets1), where each entity facet is represented as a mixture of latent topics. Then, given a document associated with multiple entities, we assume multiple entity-centric representations, where each entity-centric representation is a mixture of entity facets for each entity. Finally, a novel graphical model, the Entity Facet Topic Model (EFTM), is proposed in order to learn entity-centric document representations, entity facets, and latent topics.Through experimentation we confirm that (1) entities are multi-faceted concepts which we can model and learn, (2) a multi-faceted entity-centric modeling of documents can lead to effective representations, which (3) can have an impact in downstream application, and (4) considering a small number of facets is effective enough. In particular, we visualize entity facets within a set of documents, and demonstrate that indeed different sets of documents reflect different facets of entities. Further, we demonstrate that the proposed entity facet topic model generates better document representations in terms of perplexity, compared to state-of-the-art document representation methods. Moreover, we show that the proposed model outperforms baseline methods in the application of multi-label classification. Finally, we study the impact of EFTM’s parameters and find that a small number of facets better captures entity specific topics, which confirms the intuition that on average an entity has a small number of facets reflected in documents. 相似文献
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《Information processing & management》2020,57(4):102239
Expert finding addresses the task of retrieving and ranking talented people on the subject of user query. It is a practical issue in the Community Question Answering networks. Recruiters looking for knowledgeable people for their job positions are the most important clients of expert finding systems. In addition to employee expertise, the cost of hiring new staff is another significant concern for organizations. An efficient solution to cope with this concern is to hire T-shaped experts that are cost-effective. In this study, we have proposed a new deep model for T-shaped experts finding based on Convolutional Neural Networks. The proposed model tries to match queries and users by extracting local and position-invariant features from their corresponding documents. In other words, it detects users’ shape of expertise by learning patterns from documents of users and queries simultaneously. The proposed model contains two parallel CNN’s that extract latent vectors of users and queries based on their corresponding documents and join them together in the last layer to match queries with users. Experiments on a large subset of Stack Overflow documents indicate the effectiveness of the proposed method against baselines in terms of NDCG, MRR, and ERR evaluation metrics. 相似文献
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《Information processing & management》2023,60(5):103464
Existing approaches to learning path recommendation for online learning communities mainly rely on the individual characteristics of users or the historical records of their learning processes, but pay less attention to the semantics of users’ postings and the context. To facilitate the knowledge understanding and personalized learning of users in online learning communities, it is necessary to conduct a fine-grained analysis of user data to capture their dynamical learning characteristics and potential knowledge levels, so as to recommend appropriate learning paths. In this paper, we propose a fine-grained and multi-context-aware learning path recommendation model for online learning communities based on a knowledge graph. First, we design a multidimensional knowledge graph to solve the problem of monotonous and incomplete entity information presentation of the single layer knowledge graph. Second, we use the topic preference features of users’ postings to determine the starting point of learning paths. We then strengthen the distant relationship of knowledge in the global context using the multidimensional knowledge graph when generating and recommending learning paths. Finally, we build a user background similarity matrix to establish user connections in the local context to recommend users with similar knowledge levels and learning preferences and synchronize their subsequent postings. Experiment results show that the proposed model can recommend appropriate learning paths for users, and the recommended similar users and postings are effective. 相似文献
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Most existing search engines focus on document retrieval. However, information needs are certainly not limited to finding relevant documents. Instead, a user may want to find relevant entities such as persons and organizations. In this paper, we study the problem of related entity finding. Our goal is to rank entities based on their relevance to a structured query, which specifies an input entity, the type of related entities and the relation between the input and related entities. We first discuss a general probabilistic framework, derive six possible retrieval models to rank the related entities, and then compare these models both analytically and empirically. To further improve performance, we study the problem of feedback in the context of related entity finding. Specifically, we propose a mixture model based feedback method that can utilize the pseudo feedback entities to estimate an enriched model for the relation between the input and related entities. Experimental results over two standard TREC collections show that the derived relation generation model combined with a relation feedback method performs better than other models. 相似文献
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Collaborative information retrieval involves retrieval settings in which a group of users collaborates to satisfy the same underlying need. One core issue of collaborative IR models involves either supporting collaboration with adapted tools or developing IR models for a multiple-user context and providing a ranked list of documents adapted for each collaborator. In this paper, we introduce the first document-ranking model supporting collaboration between two users characterized by roles relying on different domain expertise levels. Specifically, we propose a two-step ranking model: we first compute a document-relevance score, taking into consideration domain expertise-based roles. We introduce specificity and novelty factors into language-model smoothing, and then we assign, via an Expectation–Maximization algorithm, documents to the best-suited collaborator. Our experiments employ a simulation-based framework of collaborative information retrieval and show the significant effectiveness of our model at different search levels. 相似文献
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[目的/意义] 随着人们对检索文档之间关联关系的理解越来越多样化和细粒度化,检索文档内信息单元间关联关系的构建显得越来越重要。本研究旨在以学术文档内信息单元间关联关系为基础,构建文档的细粒度聚合与关联机制。[方法/过程] 本研究从跨体裁聚合单元知识体系所蕴涵的各类关联关系出发,从信息组在的角度阐述支持情景和语义关联的细粒度聚合理论框架、知识组织系统构建和聚合单元元数据标注等关键问题,并提出聚合机制。[结果/结论] 研究认为构建蕴含聚合单元语义关系、学科领域语义关系、任务和文本关系的本体,采用可反应聚合单元层级与关联关系的聚合单元元数据,是细粒度聚合机制发挥效用的关键。 相似文献
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One of the most important opinion mining research directions falls in the extraction of polarities referring to specific entities (aspects) contained in the analyzed texts. The detection of such aspects may be very critical especially when documents come from unknown domains. Indeed, while in some contexts it is possible to train domain-specific models for improving the effectiveness of aspects extraction algorithms, in others the most suitable solution is to apply unsupervised techniques by making such algorithms domain-independent and more efficient in a real-time environment. Moreover, an emerging need is to exploit the results of aspect-based analysis for triggering actions based on these data. This led to the necessity of providing solutions supporting both an effective analysis of user-generated content and an efficient and intuitive way of visualizing collected data. In this work, we implemented an opinion monitoring service implementing (i) a set of unsupervised strategies for aspect-based opinion mining together with (ii) a monitoring tool supporting users in visualizing analyzed data. The aspect extraction strategies are based on the use of an open information extraction strategy. The effectiveness of the platform has been tested on benchmarks provided by the SemEval campaign and have been compared with the results obtained by domain-adapted techniques. 相似文献
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Salvatore Andolina Khalil Klouche Tuukka Ruotsalo Patrik Floréen Giulio Jacucci 《Information processing & management》2018,54(6):1182-1202
Collaborative and co-located information access is becoming increasingly common. However, fairly little attention has been devoted to the design of ubiquitous computing approaches for spontaneous exploration of large information spaces enabling co-located collaboration. We investigate whether an entity-based user interface provides a solution to support co-located search on heterogeneous devices. We present the design and implementation of QueryTogether, a multi-device collaborative search tool through which entities such as people, documents, and keywords can be used to compose queries that can be shared to a public screen or specific users with easy touch enabled interaction. We conducted mixed-methods user experiments with twenty seven participants (nine groups of three people), to compare the collaborative search with QueryTogether to a baseline adopting established search and collaboration interfaces. Results show that QueryTogether led to more balanced contribution and search engagement. While the overall s-recall in search was similar, in the QueryTogether condition participants found most of the relevant results earlier in the tasks, and for more than half of the queries avoided text entry by manipulating recommended entities. The video analysis demonstrated a more consistent common ground through increased attention to the common screen, and more transitions between collaboration styles. Therefore, this provided a better fit for the spontaneity of ubiquitous scenarios. QueryTogether and the corresponding study demonstrate the importance of entity based interfaces to improve collaboration by facilitating balanced participation, flexibility of collaboration styles and social processing of search entities across conversation and devices. The findings promote a vision of collaborative search support in spontaneous and ubiquitous multi-device settings, and better linking of conversation objects to searchable entities. 相似文献
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Graph-based recommendation approaches use a graph model to represent the relationships between users and items, and exploit the graph structure to make recommendations. Recent graph-based recommendation approaches focused on capturing users’ pairwise preferences and utilized a graph model to exploit the relationships between different entities in the graph. In this paper, we focus on the impact of pairwise preferences on the diversity of recommendations. We propose a novel graph-based ranking oriented recommendation algorithm that exploits both explicit and implicit feedback of users. The algorithm utilizes a user-preference-item tripartite graph model and modified resource allocation process to match the target user with users who share similar preferences, and make personalized recommendations. The principle of the additional preference layer is to capture users’ pairwise preferences, provide detailed information of users for further recommendations. Empirical analysis of four benchmark datasets demonstrated that our proposed algorithm performs better in most situations than other graph-based and ranking-oriented benchmark algorithms. 相似文献