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A Study on Recommendation Categories in Academic D-library

机译：高校数字图书馆推荐类别研究

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摘要

Users increasingly enjoy unprecedented access to varied and huge number of digital resources provided by the academic D-libraries to enrich their education and knowledge. As an academic digital libraries' contents become huger, it is difficult for users to obtain the needed information resources accurately and quickly. Thus, users expect more sophisticated services from digital library systems such as easy to retrieve relevant resources. One effective solution to handle this issue is to make use of recommendation service. The aim of this study is to investigate on the recommender system categories used in academic D-libraries. The paper review the most important categories including collaborative filtering, content filtering and hybrid filtering with their major strengths and limitations. Then, issues and challenges related to these categories are presented, followed by a discussion of solutions proposed by researchers to mitigate these challenges. Finally, based on the survey, a future research possibilities to develop high-quality recommender systems for academic D-libraries is presented.
机译：用户越来越多地享受到学术D图书馆提供的各种数字资源的前所未有的访问权限，以丰富他们的教育和知识。随着学术数字图书馆的内容越来越庞大，用户难以准确，快速地获取所需的信息资源。因此，用户期望数字图书馆系统提供更复杂的服务，例如易于检索相关资源。一种有效的解决方案是利用推荐服务。这项研究的目的是调查在学术D型图书馆中使用的推荐系统类别。本文回顾了最重要的类别，包括协同过滤，内容过滤和混合过滤，它们的主要优点和局限性。然后，提出了与这些类别相关的问题和挑战，然后讨论了研究人员为缓解这些挑战而提出的解决方案。最后，根据调查结果，提出了为学术D图书馆开发高质量推荐系统的未来研究可能性。

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