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Trust-Aware Feed Ranking for Scholarly Collaboration Networks

We study how follow edges, review quality, graph proximity and freshness can be blended into an explainable feed optimized for high-signal research discovery.

Workshop Paper2024ICML AI for Science WorkshopOpen access
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topicworkauthorcommunityTrust-Aware Feed Rank...Workshop Paper ...Calibrated Review Rub...Workshop Paper ...A Signal Layer for Sc...Preprint / 2026Open-Weight Biology A...Preprint / 2026Topic-Aware Opportuni...Preprint / 2026Rhea PatelPhD CandidateSunwoo KimIndustry Resear...ETH Machine Intellige...labMIT Literature SystemsinstitutionTrusted Review Circleinvite onlyResearch Collaboration9 worksLarge Language Models7 works
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Trust-Aware Feed Ranking for Scholarly Collaboration Networks

Workshop Paper / 2024

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