推荐系统中的隐语义模型研究?

隐语义模型是一种有效的隐含语义分析技术,其核心思想是通过潜在特征联系用户和物品。本文从理论方法的角度,详细介绍了隐语义模型的工作原理、模型表示、学习方法和评价指标等,并通过具体实验分别讨论几种典型隐语义模型算法的推荐效果,以全面认识和理解该模型在推荐系统中的应用特点。     

基于稿件引文内容分析的同行评审专家遴选方法

同行评审意见是期刊编辑用稿决策的重要依据,同行评审专家的来源和遴选与评审结果的质量和公平性密切相关。编辑为稿件遴选出评审意愿、专业、能力匹配,且时间充裕的审稿专家是比较费时、费力的。本文基于投稿论文的参考文献和引用的内容、动机及情感分析,提出一种同行评审专家的遴选方法。该方法有助于“小同行”专家的发现,降低不同学术范式导致的同行评议误判率,为编辑的稿件送审工作提供参考。     

How to detect indications of potential sources of bias in peer review: A generalized latent variable modeling approach exemplified by a gender study

The universalism norm of the ethos of science requires that contributions to science are not excluded because of the contributors’ gender, nationality, social status, or other irrelevant criteria. Here, a generalized latent variable modeling approach is presented that grant program managers at a funding organization can use in order to obtain indications of potential sources of bias in their peer review process (such as the applicants’ gender). To implement the method, the data required are the number of approved and number of rejected applicants for grants among different groups (for example, women and men or natural and social scientists). Using the generalized latent variable modeling approach indications of potential sources of bias can be examined not only for grant peer review but also for journal peer review.