Dr. Lina Yao : Adversarial Learning in Deep Learning based Recommender Systems

Presenter: Lina Yao

Abstract:  Deep neural networks have been demonstrating its effectiveness in recommender systems research. Recently, adversarial learning have garnered increasing interest and been leading to a surging enthusiasm for applying adversarial learning to improve recommendation performance from different aspects, including raising model robustness, alleviating data sparsity, generating initial profiles for cold-start users or items. In this talk, I will briefly introduce our recent research progress on how the adversarial learning is leveraged to alleviate multiple challenges of deep learning based recommender systems, in terms of dealing with sparse and missing data, and data noise (passive and active noise) for robustness of recommender systems.

BiographyDr. Lina Yao is currently a Scientia Associate Professor at University of New South Wales (UNSW), Australia. Her research lies in data mining and machine learning with focus on recommender systems, activity recognition, Brain Computer Interface and Internet of Things. She has published over 150 peer-reviewed papers in prestigious journals and top international conferences in the areas of data mining, machine learning and intelligent systems including ACM CACM, ACM CUSR, IEEE TMC, ACM TIST, IEEE TKDE, ACM TKDD, ACM TOIT, PR, IEEE TNSRE, IEEE TNNLS, IEEE CYB, IEEE IIT, JBHI, NeurIPS, SIGKDD, ICDM, UbiComp, AAAI, SIGIR, IJCAI and CIKM. She is serving as the Associate Editor for ACM Transactions on Sensor Networks (TOSN).