Sang-Wook Kim 
Professor at the Department of Computer Science & Engineering at Hanyang University, Seoul, Korea and the Director of the Brain-Korea-21-Plus research program.

Talk Title: Recommendation Systems: Concepts, Techniques, and Applications (Download presentation materials here)

Abstract: As the number of online items significantly grows, it becomes a difficult task for users to find those items on their own. Good matching of users to suitable items is critical to enhance user satisfaction, which highlights the importance of recommendation systems. The recommendation systems analyze users’ past behavioral characteristics, predicting the items with which a user would be truly satisfied. The approaches to recommendation systems are classified into two categories: content-based and collaborative filtering (CF) based approaches. In this talk, we discuss the concepts, techniques, and applications of recommendation systems. We start with the concepts of recommendation systems and introduce a variety of their applications. Next, we describe machine learning techniques applied in developing recommendation systems. Finally, we share the state-of-the-art techniques developed in our laboratory recently and show their effectiveness in terms of user satisfaction.

About Sang-Wook Kim

Sang-Wook Kim received his Ph.D. degree in Computer Science from Korea Advanced Institute of Science and Technology (KAIST) in 1994. From 1995 to 2003, he served as an Associate Professor of the Division of Information and Communications Engineering at Kangwon National University. In 2003, he joined Hanyang University, Seoul, Korea, where he currently is a Professor at the Department of Computer Science & Engineering and the director of the Brain-Korea-21-Plus research program. He has also been leading a National Research Lab Project from 2015. His research interests include databases, data mining, social network analysis, recommendation, and web data analysis.

From 2009 to 2010, Dr. Kim visited the Computer Science Department at Carnegie Mellon University as a Visiting Professor. From 1999 to 2000, he worked with the IBM T. J. Watson Research Center 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 120 papers in refereed international journals and international conference proceedings. He served Program Committees of over 100 international conferences including IEEE ICDE, VLDB, WWW, and ACM CIKM. He is now an associate editor of two international journals: Information Sciences and Computer Science & Information Systems (ComSIS). He received the Presidential Award of Korea in 2017 for his academic achievement and he is currently a member of National Academy of Engineering of Korea from 2019. He is also a member of the ACM and the IEEE.

Feng Xia
Full Professor in School of Software, Dalian University of Technology (DUT), China, the Director of The Alpha Lab (, and Deputy Dean (Research) of School of Software.

Talk Title: Scholarly Social Computing (Download presentation materials here)

Abstract: In an era of big data, it is not surprising that the amount of scholarly data is increasing at an unprecedented speed. Recent years have witnessed the exponential growth of data in all scientific disciplines as a result of various scholarly activities. The availability of scholarly big data makes it possible to, e.g., profile scholars and institutions in numerous dimensions, discover relationships between scholars, and understand the nature of science. In particular, this has led to the emergence of scholarly social computing, which promises to open up exciting new opportunities to work towards a better understanding of the academic society at different levels. For instance, social relationships of scholars could be derived from analysis and mining of scholarly big data. Such opportunities could transform the way how we analyze, understand, and address a lot of critical challenges facing the academic society. This talk will give an overview of scholarly social computing while discussing relevant opportunities and challenges. Special attention will be given to the understanding of academic social networks by means of big data analysis and mining. Some recent advancements in this field will be showcased.

About Feng Xia

Dr. Feng Xia is currently a Full Professor in School of Software, Dalian University of Technology (DUT), China. He is founding Director of The Alpha Lab (, and Deputy Dean (Research) of School of Software. He is/was on the Editorial Boards of over 10 int’l journals. He has served as the General Chair, Program Committee Chair, Workshop Chair, or Publicity Chair of over 30 int’l conferences and workshops, and Program Committee Member of over 50 conferences. Dr. Xia has authored/co-authored two books, over 290 scientific papers in int’l journals and conferences (such as IEEE TC, TMC, TBD, TCSS, TPDS, TETC, THMS, TVT, TII, TIE, IEEE/ACM TON, ACM TOMM, WWW, JCDL, MobiCom, and INFOCOM) and 3 book chapters, and has edited 3 int’l conference proceedings and 6 books (in Chinese). He has an h-index of 43, an i10-index of 158, and a total of more than 7000 citations to his work according to Google Scholar. His name has been included on Elsevier’s Most Cited Chinese Researchers for five consecutive years (2014-2018; ever since its inaugural version). Dr. Xia received a number of prestigious awards, including WWW 2017 Best Demo Award, IEEE DataCom 2017 Best Paper Award, and IEEE UIC 2013 Best Paper Award. He has been invited as Keynote Speaker at six international conferences and delivered a number of Invited Talks at international conferences and many universities worldwide. His research interests include data science, knowledge management, and systems engineering. He is a Senior Member of IEEE and ACM.