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数学系Seminar第1732期 A Sub-one Quasi-norm-based Similarity Measure and Related Optimization Models

Created Date 12/6/2018 惠英   View Numbers  68 Return    
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报告主题:A Sub-one Quasi-norm-based Similarity Measure and Related Optimization Models
报告人:方述诚  教授  (美国北卡州立大学)
报告时间:2018年12月19日(周三)14:10
报告地点:校本部G508
邀请人:白延琴
主办部门:理学院数学系
报告摘要:This talk introduces a sub-one quasi-norm-based similarity measure, which may effectively drive the widely adopted collaborative filtering method with fully or sparsely co-rated data sets for on-line recommender systems. Related optimization models and solution methods are also studied.

 


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