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Using adaptive resonance theory and data-mining techniques for materials recommendation based on the e-library environment

机译：使用自适应共振理论和数据挖掘技术基于电子图书馆环境进行材料推荐

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摘要

Purpose - The purpose of this research is to illustrate the use of artificial neural network (ANN) and data-mining (DM) technologies as a good approach for satisfying the requirements of library users. Design/methodology/approach - This research presents the Intelligent Library Materials Recommendations System (ILMRS) which uses the adaptive resonance theory (ART) network to distribute readers into different clusters according to their personal background. When clusters of related personal background have been established, the Apriori algorithm is used to discover the suitable materials in which readers are interested and which they may need. Findings - The investigation results indicate that the ART and Apriori mining techniques can be used to improve the accuracy of the recommendations for reading materials in the library. Moreover,rnreaders can be divided by means of demographic variables into three segments. Finally, the questionnaire survey proved that the proposed recommender system will be a suitable approach for stimulating the readers' motivation and interest. Research limitations/implications - This research is limited by its datasets from a digital libraryrnof a university in Taiwan, and it is applied by ART and Apriori mining techniques to recommend materials of readers. Originality/value - Today, digital information is becoming ever more popular. The huge quantity and the diversity of digital information are its main features. Therefore, readers are interested in obtaining useful information in an efficient manner. In this research, a digital library can use this approach to anticipate a reader's needs in advance based on the mining results.
机译：目的-这项研究的目的是说明如何使用人工神经网络（ANN）和数据挖掘（DM）技术来满足图书馆用户的需求。设计/方法/方法-这项研究提出了智能图书馆资料推荐系统（ILMRS），该系统使用自适应共振理论（ART）网络根据读者的个人背景将读者分配到不同的组中。建立了相关个人背景的集群后，Apriori算法将用于发现读者感兴趣并可能需要的合适材料。调查结果-研究结果表明，ART和Apriori挖掘技术可用于提高阅读图书馆推荐材料的准确性。此外，可以通过人口统计学变量将阅读器分为三个部分。最后，问卷调查证明，建议的推荐系统将是激发读者动机和兴趣的合适方法。研究局限性/含义-这项研究受到台湾一所大学数字图书馆的数据集的限制，并且被ART和Apriori挖掘技术应用于推荐读者的材料。创意/价值-今天，数字信息变得越来越流行。数字信息的巨大数量和多样性是其主要特征。因此，读者有兴趣以有效的方式获得有用的信息。在这项研究中，数字图书馆可以使用这种方法根据挖掘结果提前预测读者的需求。

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