讲座题目：推荐系统中基于拟模的协同过滤相似度度量

主讲人：姜珊（北卡罗来纳州立大学系统与工业工程系博士研究生）

讲座时间：3月25日（周一）下午4:30-5:30

讲座地点：嘉庚二203

主持人：缪朝炜（澳门永利yl6776管理科学系教授）

讲座摘要：Collaborative filtering (CF) is one of the most successful approaches for an online store to make personalized recommendations through its recommender systems. A neighborhood-based CF method makes recommendations to a target customer based on the similar preference of the target customer and those in the database. Similarity measuring between users directly contributes to an effective recommendation. In this paper, we propose a sub-one quasi-norm-based similarity measure for collaborative filtering in a recommender system. The proposed similarity measure shows its advantages over those commonly used similarity measures in the literature by making better use of rating values and deemphasizing the dissimilarity between users. Computational experiments using various real-life datasets clearly indicate the superiority of the proposed similarity measure, no matter in fully co-rated, sparsely co-rated or cold-start scenarios.

主讲人简介：姜珊，北卡罗来纳州立大学系统与工业工程系博士研究生。主要研究方向包括数据分析，优化问题。曾在Information Sciences，Optimization Letters等期刊发表学术论文。