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cs.IR2026
Empowering Compact LLMs with Fusion of Layer-wise Exits for Recommendation
Xurong Liang, Tong Chen, Quoc Viet Hung Nguyen +3
Large language model-based recommender systems (LLM-RSs) have demonstrated remarkable capabilities, but are computationally unsustainable for many real-world applications. Compact…
cs.IR2026
Federated Learning and Unlearning for Recommendation with Personalized Data Sharing
Liang Qu, Jianxin Li, Wei Yuan +4
Federated recommender systems (FedRS) have emerged as a paradigm for protecting user privacy by keeping interaction data on local devices while coordinating model training through…