Sang-Wook Kim, Department of Computer Science & Engineering, Hanyang University, Seoul, Republic of Korea

Title
Issues and Techniques in Modern Recommender Systems

Abstract
These days, we have a huge number of online items such as products, content, and people around us, which makes each user face difficulties in choosing the items that s/he really likes. Good matching of each individual user to her/his preferable items is a very important task to enhance users’ satisfaction and companies’ profit, highlighting the necessity of recommendation systems. The recommendation system analyzes the characteristics of users’ past behaviors and then predicts the items on which individual users would be truly satisfied based on the analysis result. In this talk, we first introduce recommendation systems and discuss their key issues and techniques. We start with the concept of recommendation systems and introduce their real-world applications in a variety of business fields. Next, we classify recommendation systems into three categories: content-based, collaborative-filtering-based, and trust-based approaches. Then, we describe a variety of machine-learning techniques employed in recommendation systems in order to provide users with better experiences. Finally, we present the state-of-the-art techniques for recommender systems recently developed at Hanyang University and show their effectiveness and efficiency with experimental results obtained from the extensive evaluation.

About the Speaker
Sang-Wook Kim received his Ph.D. degree in Computer Science from Korea Advanced Institute of Science and Technology (KAIST) in 1994. In 2003, he joined Hanyang University, Seoul, Korea, where he currently is a Professor at the Department of Computer Science & Engineering and has been recognized as a (research) Distinguished Professor at Hanyang University in 2019. He is a director of the Brain-Korea-21 research program since 2014, and also has been leading a National Research Lab and SW STAR Lab Projects from 2015 and 2022, respectively. His research interests include databases, data mining, social network analysis, recommendation, and web data analysis. From 2009 to 2010, Professor 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 the author of over 150 papers in refereed international journals and international conference proceedings. He served on Program Committees of over 100 international conferences including ACM KDD, ACM SIGIR, IEEE ICDE, IEEE ICDM, 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 the National Academy of Engineering of Korea since 2019. He is also a member of the ACM and the IEEE.

Salvador García, Department of Computer Science and Artificial Intelligence, University of Granada, Granada, Spain

Title
Monotonic Ordinal Classification: A Path to Fairness in Machine Learning Prediction

Abstract
In this keynote speech, we will explore the concept of monotonic ordinal classification and its relationship to fairness in machine learning prediction. Monotonic ordinal classification is a type of machine learning task that aims to predict outcomes based on ordinal categories or rankings ensuring compliance with existing monotonicity constraints between examples. It has gained increasing attention in recent years due to its ability to incorporate domain knowledge into the classification process in the context of fairness in machine learning.

Fairness in machine learning prediction refers to the absence of bias and discrimination in the outcomes generated by a machine learning model. A machine learning model is considered fair if it treats all individuals or groups equitably, without unfairly favoring or disadvantaging any particular group based on their demographic or other personal characteristics.

We will present recent research on the use of monotonic ordinal classification in fair machine learning prediction. This includes approaches for ensuring fairness in the training and testing of models, as well as techniques for assessing and addressing bias in the predictions. Furthermore, we will present recent research on the use of monotonic ordinal classification in fairness-aware machine learning. One approach that has shown promise in promoting fairness is monotonic ordinal classification, which enforces monotonicity constraints on the output of the model to ensure that similar individuals receive similar predictions. Finally, we will discuss the potential implications of this research for real-world applications, such as in lending and employment decisions.

Overall, this speech aims to highlight the potential of monotonic ordinal classification as a tool for achieving fairness in machine learning prediction, and to stimulate further research in this exciting and important area.

About the Speaker
Salvador García received the B.S. and Ph.D. degrees in Computer Science from the University of Granada, Granada, Spain, in 2004 and 2008, respectively. He is currently a Full Professor in the Department of Computer Science and Artificial Intelligence, University of Granada, Granada, Spain. Dr. García has published more than 120 papers in international journals (more than 85 in Q1), h-index 62. As edited activities, he is Editor in Chief of “Information Fusion” (Elsevier), and an associate editor of “Swarm and Evolutionary Computation” (Elsevier), “Machine Learning” (Springer) and “AI Communications” (IOS Press) journals. He is a co-author of the books entitled “Data Preprocessing in Data Mining”, “Learning from Imbalanced Data Sets” and “Big Data Preprocessing: Enabling Smart Data” published by Springer. His research interests include data science, data preprocessing, Big Data, evolutionary learning, Deep Learning, metaheuristics and FATE: Fairness, Accountability, Transparency and Ethics in Machine Learning. He belonged to the list of the Highly Cited Researchers in the area of Computer Sciences (2014-2020): http://highlycited.com/ (Clarivate Analytics).