单位：美国 Minnesota大学

报告时间：2018年6月26日下午13：30

地点：下沙校区教学D楼204室

摘要：

Personalized prediction predicts a user's preference for a large number ofitems through user-specific as well as content-specific information, based on a very small amount of observed preference scores. The problem of this kind involves unknown parameters of high-dimensionality in the presence of a high percentage of missing observations. In this situation,the predictive accuracy depends on how to pool the information from similar users and items. Two major approaches are collaborative filtering and content-based filtering. Whereas the former utilizes the information on users that think alike for a specific item, the latter acts on characteristics of the items that a user prefers, on which two kinds of recommender systems Grooveshark and Pandora are built. In this talk, I will present our recent research on regularized latent-factor modeling and compare with state-of-art recommenders in terms of predictive performance. Special attention will be devoted to the impact of nonignorable missing and social networks on personalized prediction.

报告人简介：

沈晓彤是明尼苏达大学John Black Johnston 杰出教授，2011年被选为美国科学促进会会士（Fellow AAAS），2006年当选美国数理统计学会会士（Fellow IMS），2004年就当选为美国统计学会会士（Fellow ASA）。

他的研究兴趣包括机器学习和数据挖掘，基于似然性推理，半参数和无参数模型，模型选择与平均。最近致力于高维数据分析的研究。他的工作涉及的应用领域是生物医药科学和工程。沈晓彤还担任许多重要刊物的主编，如Journal of machine learning research， The annals of statistic， Journal of American statistical association， Statistica Sinica， Journal of computational and Graphical statics， Statics survey， Proceedings of the National Academy of Science等。