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论文编号：	14461
作者编号：	2120213475
上传时间：	2024/3/4 11:20:13
中文题目：	科研生命周期理论视角下学术资源个性化推荐服务研究
英文题目：	Research on Personalized Recommendation Service of Academic Resources from the Perspective of Research Lifecycle Theory
指导老师：	冯湘君
中文关键字：	学术资源平台；个性化推荐；科研生命周期；信息需求；科研人员
英文关键字：	Academic Resource Platform; Personalized Recommendation; Research Lifecycle; Information Needs; Researchers
中文摘要：	在当前背景下，随着全球化和科技进步的推动，数字化浪潮和互联网技术的蓬勃发展势不可挡。与此同时，各类学术资源也呈现出爆炸式增长，学术资源平台作为科学研究的支柱之一，汇聚了大量高质量资源，然而，如何更好地激活这些资源，以更好地满足科研人员的需求，是一个备受关注的问题。本文以学术资源平台的个性化推荐板块为研究切入点，以“人”为本，以科研人员的需求为主要关注点，探讨了信息需求及其影响因素。此外，科研生命周期理论对分析和把握科研人员的信息需求、提升相关科研服务具有重要的指导意义，因此，本文以科研生命周期作为框架，构建用户对学术资源平台个性化推荐的信息需求模型，并提出相应地优化策略。 在进行研究前，本文探索了研究背景及意义，确定了使用文献研究法、半结构化访谈和扎根理论这三个方法进行研究，并捋顺研究思路、提出本文创新点。其次阅读大量文献，对概念界定、探讨理论应用。一是系统阐述科研生命周期理论，并拟定适用于本文的科研生命周期模型；二是挖掘信息需求理论内涵和理论应用；三是对学术资源个性化推荐方法和相关研究进行总结。 在充分的理论基础铺垫下，本文对学术资源平台个性化推荐板块的科研人员信息需求进行了实证研究。围绕拟定的科研生命周期各阶段，对科研人员进行半结构化访谈，后运用扎根理论对访谈数据进行编码，分别提炼出学术资源平台个性化推荐板块的科研人员信息需求及影响因素。在对研究数据完成编码后，本文首先构建出学术资源平台个性化推荐板块科研用户信息需求模型，并详述科研生命周期不同阶段信息需求，以及围绕信息、用户、环境三个维度分析需求影响因素。其次构建出学术资源平台个性化推荐服务优化模型，并为平台个性化推荐板块提出针对性的优化策略。 最后，本文进行整体研究的总结与展望，并总结研究不足之处，供后续研究参考。
英文摘要：	In the current context, with globalization and scientific and technological progress, the wave of digitization and the booming development of Internet technology are unstoppable. At the same time, all kinds of academic resources have also shown explosive growth, academic resource platforms, as one of the pillars of scientific research, have gathered a large number of high-quality resources, however, how to better activate these resources to better meet the needs of researchers is an issue of great concern. In this paper, we take the personalized recommendation board of academic resource platform as the research entry point, and explore the information demand and its influencing factors by focusing on the needs of researchers with "people" as the main concern. In addition, the research life cycle theory is of great significance in analyzing and grasping the information needs of researchers and improving related research services. Therefore, this paper takes the research life cycle as a framework to construct a model of users' information needs for personalized recommendations on academic resource platforms and proposes corresponding optimization strategies. Before carrying out the research, this paper explores the background and significance of the study, determines the use of literature research method, semi-structured interviews and rooted theory, and smoothes out the idea of the study and puts forward the innovative points of this paper. Secondly, it reads a large amount of literature to define the concepts and explore the application of theories. Firstly, the research life cycle theory is systematically elaborated and the research life cycle model applicable to this paper is formulated; secondly, the connotation and application of the information demand theory is explored; thirdly, the method of personalized recommendation of academic resources and related studies are summarized. With sufficient theoretical foundation, this paper conducts empirical research on the information demand of researchers in the personalized recommendation section of academic resource platform. Semi-structured interviews were conducted with researchers around the formulated stages of the research life cycle, and then the interview data were coded using the rooting theory to refine the information needs of researchers in the personalized recommendation section of the academic resource platform and the influencing factors, respectively. After coding the research data, this paper firstly constructs a model of research users' information demand in the personalized recommendation section of the academic resource platform, and details the information demand at different stages of the research life cycle, as well as analyzes the influencing factors of the demand around the three dimensions of information, user, and environment. Secondly, the optimization model of personalized recommendation service of academic resource platform is constructed, and targeted optimization strategies are proposed for the personalized recommendation board of the platform. Finally, this paper summarizes the overall research and outlook, and summarizes the shortcomings of the research for the reference of subsequent research.
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