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Many touted advancements in group recommendation collapse under tie-aware evaluation, reshaping our understanding of model performance.
Maintaining the relational geometry of collaborative embeddings can significantly enhance the performance of LLM-based recommendation systems, countering the detrimental effects of modality bias.
Byzantine-robust federated learning no longer needs to trade off convergence speed and model utility, even with a large number of malicious clients.
