IGNiteR: News Recommendation in Microblogging Applications
Yuting Feng, Bogdan Cautis · 2022 IEEE International Conference on Data Mining (ICDM) · 2022
As social media, and particularly microblogging applications like Twitter or Weibo, gains popularity as platforms for news dissemination, personalized news recommendation in this context becomes a significant challenge. We propose a diffusion and influence-aware approach, Influence-Graph News Recommender (IGNiteR), which is a content-based deep recommendation model that jointly exploits all the data facets that may impact adoption decisions, namely semantics, diffusion-related features pertaining to local and global influence among users, temporal attractiveness, and timeliness, as well as dynamic user preferences. We perform extensive experiments on two real-world datasets, showing that IGNiteR outperforms the state-of-the-art deep-learning based news recommendation methods.