Seminar by Professor Wang Yan, Macquarie University, Australia, RTP Harvard Room

Time: 23 Jan 2025, 3.00pm to 4.00pm

Venue: Research Techno Plaza (RTP), Level 2, Harvard Room

Title: Utilizing Recommender System Techniques to Fight Fake News

Bio: Dr. Yan Wang is currently a Full Professor in the School of Computing, Macquarie University, Australia. He received his PhD from Harbin Institute of Technology (HIT), P. R. China. Prior to joining Macquarie University in 2003, he worked as a Postdoctoral Fellow/Research Fellow in the Department of Computer Science, School of Computing, National University of Singapore (NUS). He has published a number of research papers in international conferences including AAAI, AAMAS, ICDE, IJCAI, KDD, NeurIPS, SIGIR, WWW, and journals including CSUR, TIST, TKDD, TKDE, TSC and TWEB. His research interests cover recommender systems, trust management, social computing and service computing.

Prof. Wang has served on the editorial board of several international journals, including IEEE Transactions on Services Computing (TSC), Service-Oriented Computing & Applications (SOCA) by Springer, and Human-centric Computing and Information Sciences (HCIS) by Springer. He also served as a General co-Chair of IEEE ATC2013, IEEE ATC2014, IEEE MS2015, IEEE ICWS2016 and IEEE CLOUD2017, a Program co-Chair of IEEE SCC2011, ATC2011, IEEE MS2014, IEEE SCC2018, IEEE SOSE2018 and IEEE SCC2019, and a Local Organisation Chair of IEEE DSAA2020.

Prof. Wang's research team has received a number of awards, including four Best (Student) Paper Awards from IEEE SCC2010, IEEE TrustCom2012, IEEE ICWS2016 and IEEE DSAA2024 respectively, Vice-Chancellor's (University President) Commendation for Academic Excellence in PhD thesis for 4 times (2017, 2020, 2020, 2024), and the nomination for Australasian Distinguished Doctoral Dissertation for 3 times. Prof. Wang received 2017 IEEE TC-TVSC Outstanding Service Award from the IEEE Technical Committee on Services Computing (TC-SVC), IEEE Computer Society.

Abstract: Recommender System aims to predict to what extent a user may like an item based on historical data. In addition, fake news detection and mitigation are long-term challenging tasks. This talk will introduce some studies that utilize recommender system techniques to fight fake news.

First, this talk will introduce a novel solution, which is the first in the literature to introduce recommender system technique to fake news mitigation. The proposed solution can differentiate the events behind news, identify the veracity of news, and recommend true news to users based on their historical data. Second, this talk will introduce a novel solution for unbiased and true news recommendation. It can not only capture users’ high- and low-level interests, enhancing next-news recommendation accuracy, but also effectively separate polarity and veracity information from news contents and model them more specifically, promoting fairness- and truth-aware reading interest learning for unbiased and true news recommendations.