Enhancing Collaborative Filtering for Advanced Recommender Systems
报告题目：面向高级推荐系统的增强协同滤波方法
Abstract: Recommender systems play a vital role in today’s data-rich yet information-poor landscape by efficiently guiding users to relevant content. Among various recommendation technologies, collaborative filtering (CF) stands out as a foundational approach. While CF has been successfully implemented across numerous recommender systems, certain limitations still hinder its full potential. This talk explores two key strategies for strengthening CF: integrating users’ personal values into the CF model and generating synthetic rating matrices for training the model.
摘要：推荐系统在当今数据泛滥却信息匮乏的环境中发挥着至关重要的作用，它能高效引导用户获取相关内容。在众多推荐技术中，协同滤波（CF）作为一种基础性方法脱颖而出。尽管协同滤波已在诸多推荐系统中成功应用，但其仍存在若干局限性制约着性能的充分发挥。本次报告将探讨强化协同滤波的两大关键策略：一是将用户个人价值观融入协同滤波模型，二是通过生成合成评分矩阵来训练模型。
Bio: Prof. Yasufumi Takama received a Dr. Eng. Degree from the University of Tokyo, Tokyo, Japan in 1999. He was a JSPS (Japan Society for the Promotion of Science) Research Fellow from 1997 to 1999. From 1999 to 2002 he was a Research Associate at Interdisciplinary Graduate School of Science and Engineering, Tokyo Institute of Technology in Japan. From 2002 to 2005, he was an Associate Professor at Department of Electronic Systems and Engineering, Tokyo Metropolitan Institute of Technology, Tokyo, Japan. From 2005 to 2013, he was an Associate Professor at Faculty of Systems Design, Tokyo Metropolitan University, Tokyo, Japan. Since 2014, he has been a Professor at Faculty of Systems Design, Tokyo Metropolitan University, Tokyo, Japan. He also participated in PREST (Pre-cursory Research for Embryonic Science and Technology), JST (Japan Science and Technology Corporation) from 2000 to 2003. His current research interest includes information recommendation, Web intelligence, information visualization, and human in the loop. Dr. Takama is a member of IEEE, ACM, IEICE (Institute of Electronics, Information and Communication Engineers), JSAI (Japanese Society of Artificial Intelligence), and IPSJ (Information Processing Society of Japan).
简介：Yasufumi Takama教授于1999年获日本东京大学工学博士学位。1997至1999年担任日本学术振兴会（JSPS）特别研究员。1999至2002年任日本东京工业大学综合理工学研究科助理研究员；2002至2005年任日本东京都立科学技术大学电子系统工程系副教授；2005至2013年任日本东京都立大学系统设计学部副教授；2014年至今任该校系统设计学部教授。2000至2003年间，他还参与了日本科学技术振兴机构（JST）先驱性研究计划（PREST）。其当前研究方向包括信息推荐系统、网络智能、信息可视化及人机协同技术。高见博士是电气电子工程师学会（IEEE）、国际计算机学会（ACM）、日本电子情报通信学会（IEICE）、日本人工智能学会（JSAI）及日本信息处理学会（IPSJ）的会员。