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
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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