论文标题
EER：使用员工声誉的企业专家排名
EER: Enterprise Expert Ranking using Employee Reputation
论文作者
论文摘要
在线企业遍布各大洲的出现引起了该领域中专家身份的需求。本文解决了包括雇主寻求默契专业知识和雇员知识的意图的场景，本文已解决。现有的基于声誉的企业专业知识排名的方法利用Pagerank，正态分布和隐藏的Markov模型进行专业知识排名。这些模型遇到了负面推荐，勾结，声誉通货膨胀和活力的问题。然而，作者提出了一种贝叶斯方法，利用基于beta概率分布的声誉模型为企业中的员工排名。实验结果表明，与以前的技术相比，在精度和平均平均误差（MAE）方面的性能有所提高，三个数据集的平均精度将近7％。所提出的技术能够在动态环境中区分相互作用的类别。结果表明，该技术与数据的评级模式和密度无关。
The emergence of online enterprises spread across continents have given rise to the need for expert identification in this domain. Scenarios that includes the intention of the employer to find tacit expertise and knowledge of an employee that is not documented or self-disclosed has been addressed in this article. The existing reputation based approaches towards expertise ranking in enterprises utilize PageRank, normal distribution, and hidden Markov model for expertise ranking. These models suffer issue of negative referral, collusion, reputation inflation, and dynamism. The authors have however proposed a Bayesian approach utilizing beta probability distribution based reputation model for employee ranking in enterprises. The experimental results reveal improved performance compared to previous techniques in terms of Precision and Mean Average Error (MAE) with almost 7% improvement in precision on average for the three data sets. The proposed technique is able to differentiate categories of interactions in a dynamic context. The results reveal that the technique is independent of the rating pattern and density of data.
