新框架HiCore和HyCoRec解决对话推荐中的马太效应 · 跟踪4个来源

报道来源 [4]

1. arXiv cs.AI TIER_1 English(EN) · Yongsen Zheng, Ruilin Xu, Guohua Wang, Liang Lin, Kwok-Yan Lam · 2026-07-22 04:00

   缓解马修效应：多超图增强的多兴趣自监督学习用于对话推荐

   arXiv:2607.18609v1 Announce Type: cross Abstract: The Matthew effect is a big challenge in Recommender Systems (RSs), where popular items tend to receive increasing attention, while less popular ones are often overlooked, perpetuating existing disparities. Although many existing …

2. arXiv cs.AI TIER_1 English(EN) · Yongsen Zheng, Ruilin Xu, Ziliang Chen, Guohua Wang, Mingjie Qian, Jinghui Qin, Liang Lin · 2026-07-21 04:00

   HyCoRec：超图增强多偏好学习以缓解对话推荐中的马太效应

   arXiv:2607.17461v1 Announce Type: cross Abstract: The Matthew effect is a notorious issue in Recommender Systems (RSs), \emph{i.e.}, the rich get richer and the poor get poorer, wherein popular items are overexposed while less popular ones are regularly ignored. Most methods exam…

3. arXiv cs.IR (Information Retrieval) TIER_1 English(EN) · Kwok-Yan Lam · 2026-07-21 01:07

   缓解马修效应：多超图增强的多兴趣自监督学习用于对话推荐

   The Matthew effect is a big challenge in Recommender Systems (RSs), where popular items tend to receive increasing attention, while less popular ones are often overlooked, perpetuating existing disparities. Although many existing methods attempt to mitigate Matthew effect in the …

4. arXiv cs.IR (Information Retrieval) TIER_1 English(EN) · Liang Lin · 2026-07-20 01:22

   HyCoRec：超图增强多偏好学习以缓解对话推荐中的马太效应

   The Matthew effect is a notorious issue in Recommender Systems (RSs), \emph{i.e.}, the rich get richer and the poor get poorer, wherein popular items are overexposed while less popular ones are regularly ignored. Most methods examine Matthew effect in static or nearly-static reco…