Due to the exponential increase in research papers on a daily basis, finding and accessing related academic documents over the Internet is monotonous. One of the leading approaches was the use of recommendation systems to proactively recommend scholarly papers to individual researchers. The primary drawback to these methods, however, is that their success depends on user profile information and is therefore unable to provide useful suggestions to the new user. In addition, both the public and the non-public used descriptive metadata are used. The scope of the recommendation is therefore limited to a number of documents which are either publicly available or which are granted copyright permits. In alleviating the above problems, we proposed an alternative approach using public contextual metadata for an independent framework that customizes scholarly papers, regardless of the research field and user expertise. Experimental tests have shown significant improvements over other baseline methods.

中文翻译：

---

基于公共上下文元数据的研究论文推荐系统

由于研究论文每天呈指数级增长，通过互联网查找和访问相关学术文件变得单调乏味。领先的方法之一是使用推荐系统主动向个别研究人员推荐学术论文。然而，这些方法的主要缺点是它们的成功取决于用户配置文件信息，因此无法向新用户提供有用的建议。此外，公共和非公共使用的描述性元数据都被使用。因此，建议的范围仅限于一些公开可用或获得版权许可的文件。在缓解上述问题的同时，我们提出了一种使用公共上下文元数据的替代方法，用于定制学术论文的独立框架，无论研究领域和用户专业知识如何。实验测试表明比其他基线方法有显着改进。