Personalised Knowledge-aware News Recommendation

MPhil Thesis Defence


Title: "Personalised Knowledge-aware News Recommendation"

By

Mr. Haowen KE


Abstract

Online news aggregation services have become the first choice to read news for 
many internet users. However, thousands of news articles posted on a daily 
basis make it impossible for users to select intriguing news articles and keep 
track of the latest relevant topics. The automated systems are developed to 
tackle information overload Various news recommendation methods are proposed to 
provide personalized experiences to users from diversified backgrounds.

To capture the higher-order relations hidden in texts, we propose a general 
framework of personalized news recommendation which explores and exploits 
existing knowledge graphs. The model deploys a heuristic method to take 
advantage of rich knowledge crowdsourced by human editors. Furthermore, the 
pretrained language models grab the attention of the NLP community. We 
demonstrated that this framework could be easily adapted to these large-scale 
models and exploits their representation capability. In the experiments on a 
real-world recommendation dataset, our model outperforms other state-of-the-art 
models. The further case study shows how the entity paths obtained by our model 
improve the recommendation quality.


Date:  			Thursday, 15 July 2021

Time:			3:00pm - 5:00pm

Zoom meeting: 
https://hkust.zoom.us/j/91222623496?pwd=cmtOeHBsMFkvcVJNTVJhaTdab2RGZz09

Committee Members:	Dr. Yangqiu Song (Supervisor)
 			Prof. Xiaofang Zhou (Chairperson)
 			Prof. Nevin Zhang


**** ALL are Welcome ****