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cs.IR2026
Divergence Meets Consensus: A Multi-Source Negative Sampling Framework for Sequential Recommendation
Yuanzi Li, Lingjie Wang, Jingyu Zhao +4
Negative sampling is significant for training sequential recommendation models under implicit feedback. The predominant strategy, self-guided hard negative sampling, selects negati…
cs.IR2025
Explainable Recommendation with Simulated Human Feedback
Jiakai Tang, Jingsen Zhang, Zihang Tian +3
Recent advancements in explainable recommendation have greatly bolstered user experience by elucidating the decision-making rationale. However, the existing methods actually fail t…