AI Synergies

Speakers

Alireza Gharahighehi

PhD Student

News Topic Recommendation using an extended Bayesian Personalized Ranking

Bayesian Personalized Ranking (BPR) is a famous recommendation approach which learns to rank items based on one-class implicit feedback. In this study I use an extended version of BPR using consumption behavior of users on news article to recommend news topic. The extended version performs better compare to the original version of BPR.


Biography

Alireza Gharahighehi is a PhD student in Kulak (KU Leuven at Kortrijk). He started his PhD in September 2018 with focus on recommender systems. He received his first M.S in Industrial Engineering form Bu-ali Sina university and two other Masters in Business Economics and Artificial Intelligence from Ku Leuven. Now he is working on a news recommendation system project.

Program

Halle Vitree
Research presentations ML
Day 2 - Nov 7th

Brewery of Ideas

AI Synergies is organized by VUB/ULB, BNVKI and Brewery of Ideas.

More info about our events