论学术会议发言出席率的可预测性
On the Predictability of Talk Attendance at Academic Conferences
摘要：
本文主要研究了学术会议中真实世界的演讲出席率在不同影响因素下的预测。我们研究的可预测性谈话出席使用现实世界中跟踪面对面的接触。此外，我们调查和讨论的预测能力的用户兴趣提取用户以前的出版物。我们应用混合根PageRank，一种最先进的无监督机器学习方法，它结合了来自不同来源的信息。使用这种方法，我们分析和讨论的联系人和兴趣网络的预测能力单独和组合。我们发现，接触和相似性网络实现可比的结果，不同的网络的组合只能在有限的范围内帮助提高预测质量。在我们的实验中，我们分析了使用会议管理系统Conferator收集的2011年ACM超文本和超媒体会议的可预测性。
英文摘要：
This paper focuses on the prediction of real-world talk attendances at academic conferences with respect to different influence factors. We study the predictability of talk attendances using real-world tracked face-to-face contacts. Furthermore, we investigate and discuss the predictive power of user interests extracted from the users' previous publications. We apply Hybrid Rooted PageRank, a state-of-the-art unsupervised machine learning method that combines information from different sources. Using this method, we analyze and discuss the predictive power of contact and interest networks separately and in combination. We find that contact and similarity networks achieve comparable results, and that combinations of different networks can only to a limited extend help to improve the prediction quality. For our experiments, we analyze the predictability of talk attendance at the ACM Conference on Hypertext and Hypermedia 2011 collected using the conference management system Conferator.
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。
