WEBSHORTS dataset and SHORTS-CAST framework ground micro-video popularity prediction in structured open-web context collected at upload time and enable selective online adaptation using delayed labels.
Echoes in the feed: Evolution- aware prompt-augmented micro-video popularity prediction
3 Pith papers cite this work, alongside 1 external citations. Polarity classification is still indexing.
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A survey that organizes fairness research in LLM-based recommender systems via a two-dimensional taxonomy of bias mechanisms and fairness targets while linking to other trustworthy AI concerns.
Synthetically formalizing information needs into topics with descriptions and narratives improves LLM relevance assessor agreement with humans and reduces over-labeling of relevant documents on TREC Deep Learning and Robust04.
citing papers explorer
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Will It Go Viral? Grounding Micro-Video Popularity Prediction on the Open Web
WEBSHORTS dataset and SHORTS-CAST framework ground micro-video popularity prediction in structured open-web context collected at upload time and enable selective online adaptation using delayed labels.
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Rethinking Fairness in LLM-Based Recommender Systems: A Survey
A survey that organizes fairness research in LLM-based recommender systems via a two-dimensional taxonomy of bias mechanisms and fairness targets while linking to other trustworthy AI concerns.
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Formalized Information Needs Improve Large-Language-Model Relevance Judgments
Synthetically formalizing information needs into topics with descriptions and narratives improves LLM relevance assessor agreement with humans and reduces over-labeling of relevant documents on TREC Deep Learning and Robust04.