On Knowledge Graph and Causality for Recommender Systems

Overview

Abstract

Knowledge Graph (KG) is a large-scale semantic network consisting of entities/concepts as well as the semantic relationships among them, which could be considered as a concise version of Semantic Web. Recently KG is emerging as a hot topic of knowledge discovery and management under artificial intelligence, facilitating semantic computing. Causal relation is a reflection of user behaviours with backend intention, which is related another emerging hot topic – recommendation interpretability. This talk will cover the recent research progresses in these two areas and highlight some open research challenges in recommender systems.

Brief Biography

Dr Guandong Xu is a Professor at University of Technology Sydney, specialising in Data Science and Data Analytics, Recommender Systems, Web Mining, Text mining and NLP, Social Network Analysis, and Social Media Mining. He has published three monographs, dozens of book chapters and edited conference proceedings, and 200+ journal and conference papers in decent journals and conferences. He leads Data Science and Machine Intelligence Lab at UTS. He is the assistant Editor-in-Chief of World Wide Web Journal and has been serving in editorial board or as guest editors for several international journals. He has received a number of Awards from academia and industry community, such as 2018 Top-10 Australian Analytics Leader Award.