Dipartimento di Ingegneria informatica, automatica e gestionale

We study how to improve the accuracy and running time of top-N
recommendation with collaborative filtering (CF). Unlike existing works that
use mostly rated items (which is only a small fraction in a rating matrix),
we propose the notion of pre-use preferences of users toward a vast amount
of unrated items. Using this novel notion, we effectively identify
uninteresting items that were not rated yet but are likely to receive very
low ratings from users, and impute them as zero. This simple-yet-novel
zero-injection method applied to a set of carefully-chosen uninteresting
items not only addresses the sparsity problem by enriching a rating matrix
but also completely prevents uninteresting items from being recommended as
top-N items, thereby improving accuracy greatly. As our proposed idea is
method-agnostic, it can be easily applied to a wide variety of popular CF
methods. Through comprehensive experiments using the Movielens dataset and
MyMediaLite implementation, we successfully demonstrate that our solution
consistently and universally improves the accuracies of popular CF methods
(e.g., item-based CF, SVD-based CF, and SVD++) by two to five orders of
magnitude on average. Furthermore, our approach reduces the running time of
those CF methods by 1.2 to 2.3 times when its setting produces the best
accuracy.

Bio. Sang-Wook Kim received the B.S. degree in Computer Engineering from Seoul
National University, Korea at 1989, and earned the M.S. and Ph.D. degrees in
Computer Science from Korea Advanced Institute of Science and Technology
(KAIST), Korea at 1991 and 1994, respectively. From 1995 to 2003, he served
as an Associate Professor of the Division of Computer, Information, and
Communications Engineering at Kangwon National University, Korea. In 2003,
he joined Hanyang University, Seoul, Korea, where he currently is a
Professor at the Department of Computer Science and Engineering and the
director of the Brain-Korea-21-Plus research program. He is also leading a
National Research Lab (NRL) Project funded by National Research Foundation
from 2015. His research interests include databases, data mining, multimedia
information retrieval, social network analysis, recommendation, and web data
analysis.

From 2009 to 2010, Dr. Kim visited Computer Science Department at Carnegie
Mellon University as a Visiting Research Professor. From 1999 to 2000, he
worked with the IBM T. J. Watson Research Center, USA as a Post-Doc. He also
visited the Computer Science Department of Stanford University as a Visiting
Researcher in 1991. He is an author of over 110 papers in refereed
international journals and international conference proceedings. He received
the Best Paper Award from the 29th ACM International Symposium on Applied
Computing (ACM SAC) in 2014 and the Best Poster Presentation Award from the
ACM International Conference on Information and Knowledge Management (ACM
CIKM) in 2013.  Dr. Kim was a General Co-Chair of the 6th International
Conference on Computational Collective Intelligence Technologies and
Applications in 2014, a Program Co-Chair for the ACM International
Conference on Ubiquitous Information Management and Communications (ACM
IMCOM) in 2015, a Program Co-Chair for the International Conference on
Emerging Databases in 2013, and a Track Chair for the Social Network and
Media Analysis Track in the ACM International Symposium on Applied Computing
(ACM SAC) in 2015.  He also served Program Committees of over 80
international conferences including IEEE ICDE, VLDB, WWW, and ACM CIKM. He
is now an associate editor of Information Sciences.  Dr. Kim received the
Outstanding Service Award from ACM SIGAPP and the Outstanding Contributions
Award from Database Society of Korea in 2014. He is a member of the ACM and
the IEEE.