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Conference Papers Year : 2022

IGNiteR: News Recommendation in Microblogging Applications

IGNiteR: News Recommendation in Microblogging Applications


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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Dates and versions

hal-03974529 , version 1 (06-02-2023)



Yuting Feng, Bogdan Cautis. IGNiteR: News Recommendation in Microblogging Applications. 2022 IEEE International Conference on Data Mining (ICDM), IEEE, Nov 2022, Orlando, United States. pp.939-944, ⟨10.1109/ICDM54844.2022.00111⟩. ⟨hal-03974529⟩
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