讲座时间：4月21日 15:30-17:00

讲座地点：勤园12-304

报告人简介：

王子栋，现任英国伦敦Brunel University讲席教授，欧洲科学院院士，欧洲科学与艺术院院士，IEEE Fellow，International Journal of Systems Science主编，Neurocomputing主编。多年来从事控制理论、机器学习、生物信息学等方面研究，在SCI刊物上发表国际论文七百余篇。现任或曾任十二种国际刊物的主编、副编辑或编委。曾任旅英华人自动化及计算机协会主席、东华大学国家级领军人才、清华大学国家级专家。

报告主要内容：

In this talk, we discuss a novel user-based collaborative filtering (CF) algorithm with improved performance for recommendation systems. The statistical information set (SIS) of individual rating data is, for the first time, employed to analyze the user rating habit, thereby facilitating the performance improvement of the CF algorithm. On the basis of the SIS, a new yet comprehensive similarity measure (SM) is proposed to quantify the distance between two users with focus on both the users' preferences on items and their rating habits. Compared with the traditional SM, our proposed SM is more general with clearer application insights in complicated situations. The developed CF algorithm makes full use of the known information of a recommendation system, which merits high prediction accuracy and wide application potential. The developed CF algorithm is applied to a real-world disease (Friedreich's ataxia) assessment system, where both the effectiveness and the superiority of our proposed algorithm are demonstrated.