Divergence Meets Consensus: A Multi-Source Negative Sampling Framework for Sequential Recommendation

This work studies negative sampling for sequential recommendation by combining divergent signals with consensus-driven selection across multiple sources.

Recommended citation: "Divergence Meets Consensus: A Multi-Source Negative Sampling Framework for Sequential Recommendation." SIGIR 2026. Role: second author.