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cs.IR2026
Farewell to Item IDs: Unlocking the Scaling Potential of Large Ranking Models via Semantic Tokens
Zhen Zhao, Tong Zhang, Jie Xu +5
Recent studies on scaling up ranking models have achieved substantial improvement for recommendation systems and search engines. However, most large-scale ranking systems rely on i…
cs.LG2026
FedCARE: Federated Unlearning with Conflict-Aware Projection and Relearning-Resistant Recovery
Yue Li, Mingmin Chu, Xilei Yang +5
Federated learning (FL) enables collaborative model training without centralizing raw data, but privacy regulations such as the right to be forgotten require FL systems to remove t…