MDCNS is a multi-source negative sampling framework for sequential recommendation that uses peer and teacher models plus divergence and consensus mechanisms to improve diversity and avoid local optima.
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cs.IR 2years
2026 2representative citing papers
A distilled LLM generates real-time, natural-language user interest personas—combining summarized interests with novel exploration topics—and this system produced small but significant viewer-value gains in a billion-user A/B test.
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Divergence Meets Consensus: A Multi-Source Negative Sampling Framework for Sequential Recommendation
MDCNS is a multi-source negative sampling framework for sequential recommendation that uses peer and teacher models plus divergence and consensus mechanisms to improve diversity and avoid local optima.
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LLM-Based User Personas for Recommendations at Scale
A distilled LLM generates real-time, natural-language user interest personas—combining summarized interests with novel exploration topics—and this system produced small but significant viewer-value gains in a billion-user A/B test.