商学院:Research on Group Decision Support Systems: Large Groups, Consensus, and Multi-香港六合彩开奖结果|今日特码开什么|管家婆中特网

本文地址:http://www.beatcollegeadmissions.com/2017/1107/c271a152196/page.htm
文章摘要:商学院:Research on Group Decision Support Systems: Large Groups, Consensus, and Multi, 艾瑞咨询统计数据显示,2009年1-12月主要免费电子邮箱的人均月度访问次数情况中,网易居领军地位,且远高于其他免费电子邮箱。但不容忽视的是,品牌偏好出现变化。其中,网易、腾讯、雅虎、新浪仍位列前四,合计份额达80.6%。网易已由2008年47.8%市占率下降为40%,而第二名腾讯则由20.5%升至23.7%。此外,个人免费电子邮箱满意度排名中,网易、腾讯、Gmail位列前三甲,得分分别为5.18、4.96、4.88。 但是总有一些企业在夹缝中生存下来并且壮大。这些企业就是以海尔为代表的中国企业。这些企业非常早的能认识到,没有品牌,就不会有过硬的服务,更不会在市场中让人记住你的名字。大部分没有意识到这点的中国企业想出去,可是总也出不去。在这方面,海尔的海外三步走战略给大家上了生动的一课,成了中国企业走出国门的代表。现在的中国消费者,早已经习惯了购买自己信任的品牌产品,而现在中国的企业不仅仅走出了国门。, 颇为有趣的是,啤酒除了可以画画以外,还可以用来防止头发变黄,甚至可以去除头屑。而用啤酒洗头的方法,也极为简单:只需将头发洗净、擦干,再将整瓶啤酒的1/8均匀地抹在头发上,让啤酒渗透头发根部。长此已久,啤酒中的营养成分,可以防止头发干枯,并使得头发光亮。另一方面,区别于以往采用“暴力”入侵大脑方式,主动给用户灌信息的视频营销广告1.0版本,腾讯已经演变为社交化的营销手段,更容易以符合用户价值观、思维的方式,让用户对其产生情感、共鸣,并最终主动“消化”视频广告中的传播信息。非“被动灌装”的传播方式,使得视频广告内容更易被用户收看并被记牢。窝窝团率先打破团购模式。

时间:20171124日(周五)下午2:00-4:30

地点:博学楼A100

报告人:Iván Palomares Carrascosa

报告人介绍:Iván Palomares Carrascosa is a Lecturer in Computer Science with the School of Computer Science, Electrical and Electronic Engineering, and Engineering Maths (SCEEM), University of Bristol, and visiting Professor with the Management and Economics Sciences School, University of Occidente (Mexico). He received his MSc and PhD degrees (with nationwide distinctions) from the Universities of Granada and Jaén (Spain). Iván’s research interests include AI techniques to support decision making under uncertainty, consensus building, multi-view and collaborative filtering recommender systems, human-machine decision support, fuzzy preference aggregation and data fusion. Applications of his research include management, group recommender systems, disaster management, cybersecurity and energy planning. He has co-authored 13 publications in international journals and over 30 contributions to conferences, along with his recently published co-edited Springer book “Data Analytics and Decision Support for Cybersecurity”.

报告内容:Real-life collective decision making situations typically involve added complexities such as: (I) the need for effectively handling uncertainty due to human vagueness/subjectivity in expressing preferences; (II) the presence of multiple evaluation criteria and participants with diverse background, demanding appropriate preference aggregation methods; and importantly, (III) the importance of making consensual decisions. All the above challenges accentuate in large-group decision making problems involving a large amount of diverse participants, and in group recommender systems, in which an enormous number of items and user preferences must be analysed to recommend the best product or service to a group. Both situations have increasingly become a reality in recent years, due to the rise of social network and crowd-based platforms, along with the latest advances in mobile/cloud computing.

This talk firstly introduces the main challenges of large-group decision making problems, followed by an overview of recent research trends in the topic. Particular focus is given to consensus approaches to support accepted large-group decisions. Secondly, the talk introduces multi-view data approaches in recommender systems, outlining how aggregation techniques can be potentially utilized to intelligently incorporate multiple views of information and improve recommendation processes for groups of users. The talk concludes with a series of “lessons learnt” and future directions of research.

来源: 商学院 编辑: 李海峰 责任人: