作者会议推荐的对应分析框架
A Correspondence Analysis Framework for Author-Conference Recommendations
摘要：
多年来，科学家的成就和发现是通过在适当的期刊或会议上发表的研究论文来宣传的。通常情况下，已有经验的科学家，尤其是新手，都陷入了选择一个合适的会议来完成他们的工作的困境。每一个科学会议和期刊都倾向于某个特定的研究领域，而且任何一个特定的领域都有大量的会议和期刊。选择一个合适的地点是至关重要的，因为它有助于接触到正确的观众，也进一步得到他们的论文发表的机会。在这项工作中，我们解决的问题，建议适当的会议的作者，以增加他们的机会接受。我们提出了三种不同的方法，同样涉及到使用的社会网络的作者和论文的内容在降维和主题建模的设置。在所有这些方法中，我们应用对应分析（CA）来获得相关实体之间的适当关系，例如会议和论文。与现有的基于内容的过滤、协同过滤和混合过滤等方法相比，我们的模型显示出良好的效果。
英文摘要：
For many years, achievements and discoveries made by scientists are made aware through research papers published in appropriate journals or conferences. Often, established scientists and especially newbies are caught up in the dilemma of choosing an appropriate conference to get their work through. Every scientific conference and journal is inclined towards a particular field of research and there is a vast multitude of them for any particular field. Choosing an appropriate venue is vital as it helps in reaching out to the right audience and also to further one's chance of getting their paper published. In this work, we address the problem of recommending appropriate conferences to the authors to increase their chances of acceptance. We present three different approaches for the same involving the use of social network of the authors and the content of the paper in the settings of dimensionality reduction and topic modeling. In all these approaches, we apply Correspondence Analysis (CA) to derive appropriate relationships between the entities in question, such as conferences and papers. Our models show promising results when compared with existing methods such as content-based filtering, collaborative filtering and hybrid filtering.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
