IGNiteR: News Recommendation in Microblogging Applications
IGNiteR: News Recommendation in Microblogging Applications
Abstract
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.
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