Biography
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).
Towards Extension of Collaborative Filtering for Recommender Systems
The recommendation is a technology for suggesting items of interest to users. Under the data-rich but information-poor environment in our society, recommender systems have been playing crucial roles in supporting our efficient information access. Among various technologies developed for recommender systems, collaborative filtering (CF) is definitely one of the critical technologies. Different recommender systems have been successfully developed using CF, but they still have several drawbacks that limit their applicability. This talk discusses two approaches for extending the power of CF, modeling users’ personal values and generating a synthetic rating matrix for CF, together with a brief introduction to CF.