We attempt to improve user satisfaction with the effects of retrieval results and visual appearance by employing users’ own information. User feedback on digital platforms has been proven to be one type of user cognition. Through conducting a digital literature resource organization model based on user cognition, our proposal improves both the content and presentation of retrieval systems. This paper takes Powell's City of Books as an example to describe the construction process of a knowledge network. The model consists of two parts. In the unstructured data part, synopses and reviews were recorded as representatives of user cognition. To build the resource category, linguistic and semantic analyses were used to analyze the concepts and the relationships among them. In the structural data part, the metadata of every book was linked with each other by informetrics relationships. The semantic resource was constructed to assist with building the knowledge network. We conducted a mock-up to compare the new category and knowledge-recommendation system with the current retrieval system. Thirty-nine subjects examined our mock-up and highly valued the differences we made for the improvements in retrieval and appearance. Knowledge recommendation based on user cognition was tested to be positive based on user feedback. There could be more research objects for digital resource knowledge recommendations based on user cognition.

中文翻译：

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以人为本：基于用户认知的数字文学资源知识推荐

我们试图通过利用用户自己的信息来提高用户对检索结果和视觉外观效果的满意度。数字平台上的用户反馈已被证明是用户认知的一种类型。通过建立基于用户认知的数字文献资源组织模型，我们的建议改善了检索系统的内容和表示。本文以鲍威尔的《书籍之城》为例来描述知识网络的构建过程。该模型包括两个部分。在非结构化数据部分，概要和评论被记录为用户认知的代表。为了建立资源类别，使用语言和语义分析来分析概念及其之间的关系。在结构数据部分，每本书的元数据都是通过信息计量关系相互链接的。构建语义资源以帮助构建知识网络。我们进行了模拟，以将新类别和知识推荐系统与当前检索系统进行比较。39名受试者检查了我们的模型，并高度评价了我们在检索和外观方面的改进所带来的差异。基于用户反馈的知识推荐被测试为肯定的。基于用户认知的数字资源知识推荐可能会有更多的研究对象。我们进行了模拟，以将新类别和知识推荐系统与当前检索系统进行比较。39名受试者检查了我们的模型，并高度评价了我们在检索和外观方面的改进所带来的差异。基于用户反馈的知识推荐被测试为肯定的。基于用户认知的数字资源知识推荐可能会有更多的研究对象。我们进行了模拟，以将新类别和知识推荐系统与当前检索系统进行比较。39名受试者检查了我们的模型，并高度评价了我们在检索和外观方面的改进所带来的差异。基于用户反馈的知识推荐被测试为肯定的。基于用户认知的数字资源知识推荐可能会有更多的研究对象。