About the Lecture

Causal learning has attracted a lot of research attention with the advance in explainable artificial intelligence. Causal learning contains causal discovery and causal inference two directions, where causal inference is to estimate the causal effects in treatment guided by causal graph structure and has been extended in tasks of counterfactual analysis, disentanglement learning, and debiasing. In this talk, we will introduce our new proposal of incorporating causal learning into recommender systems, and present two recent research on de-biasing confounding in recommendation and causal disentanglement for Intent Learning in Recommendation. Experimental studies on real world datasets have proven the effectiveness of the proposed models.

About the Lecturer

Dr Guandong Xu is an Australian Computer Society (ACS) Fellow and Professor at School of Computer Science, University of Technology Sydney, specialising in Data Science, Recommender Systems, and Social Computing. He has published 250+ papers in leading journals and conferences. He leads Smart Future Research Centre and Data Science and Machine Intelligence Lab at UTS. He is the Editor-in-Chief of Human-centric Intelligent Systems and assistant Editor-in-Chief of World Wide Web Journal and serving in editorial board or guest editors for several international journals. He has received several Awards from academia and industry, e.g., Top-10 Australian Analytics Leader Award and Australian Computer Society Disruptors Award.