Uslab Published a Paper Recently in IEEE Transactions on Industrial Informatics (USLab近期在期刊IEEE Transactions on Industrial Informatics发表学术论文) [2017-05-15]

发布时间:2017-05-15
 
A paper entitled 'Public Interest Analysis Based on Implicit Feedback of IPTV Users' has been published recently in IEEE Transactions on Industrial Informatics. This paper is a joint work with colleagues of University of Ljubljana, Slovenia, and Dr Yuan Zhang is the corresponding author. This paper can be accessed here.
 
Abstract: Modern information systems make it increasingly easy to gain more insight into the public interest, which is becoming more and more important in diverse public and corporate activities and processes. The disadvantage of existing research that focuses on mining the information from social networks and online communities is that it does not uniformly represent all population groups and that the content can be subjected to self-censoring or curation. In this paper, we propose and describe a framework and a method for estimating public interest from the implicit negative feedback collected from the Internet protocol television (IPTV) audience. Our research focuses primarily on the channel change events and their match with the content information obtained from closed captions. The presented framework is based on concept modeling, viewership profiling, and combines the implicit viewer reactions (channel changes) into an interest score. The proposed framework addresses both above-mentioned disadvantages or concerns. It is able to cover a much broader population, and it can detect even minor variations in user behavior. We demonstrate our approach on a large pseudonymized real-world IPTV dataset provided by an ISP, and show how the results correlate with different trending topics and with parallel classical long-term population surveys.
 
 
  最近,USLab与斯洛文尼亚University of Ljubljana的4位同行合作,在期刊IEEE Transactions on Industrial Informatics发表了论文“Public Interest Analysis Based on Implicit Feedback of IPTV Users”。张远博士是该论文通讯作者,论文提出了利用潜式IPTV观众信息(负面的反馈)来评估公众兴趣的框架和方法,全文可以在此处获取。
 
摘要:现代信息系统使洞悉公众兴趣变得越来越容易,这在各种公共和公司的活动和过程中变得越来越重要。现有的研究重点是从社会网络和在线社区中挖掘信息,它的不足之处在于,它不能统一地代表所有人口群体,而且内容可以进行自我审查或策划。在本文中,我们提出并描述了从IPTV观众收集隐含的负面的反馈来评估公众兴趣的框架和方法。我们的研究首先侧重于用户切换频道事件及其从隐藏字幕获得的内容信息的匹配。所提出的框架基于概念建模,观众分析,并将隐含的观众反应(频道变化)与兴趣分数相结合。提出的框架解决了上述缺点或疑虑。它能够覆盖更广泛的人群,并且可以检测用户行为的微小变化。我们展示了用ISP提供的大型假名现实世界IPTV数据组的方法,并展示了结果与不同趋势主题以及经典长期人口调查的相关性。