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pro vyhledávání: '"Liu, Langming"'
Autor:
Liu, Langming, Zhou, Dingxuan
Federated learning is an efficient machine learning tool for dealing with heterogeneous big data and privacy protection. Federated learning methods with regularization can control the level of communications between the central and local machines. St
Externí odkaz:
http://arxiv.org/abs/2411.01548
Autor:
Liu, Langming, Wang, Wanyu, Zhao, Xiangyu, Zhang, Zijian, Zhang, Chunxu, Lin, Shanru, Wang, Yiqi, Zou, Lixin, Liu, Zitao, Wei, Xuetao, Yin, Hongzhi, Li, Qing
Recommender systems play a pivotal role across practical scenarios, showcasing remarkable capabilities in user preference modeling. However, the centralized learning paradigm predominantly used raises serious privacy concerns. The federated recommend
Externí odkaz:
http://arxiv.org/abs/2411.01540
Autor:
Liu, Langming, Zhao, Xiangyu, Zhang, Chi, Gao, Jingtong, Wang, Wanyu, Fan, Wenqi, Wang, Yiqi, He, Ming, Liu, Zitao, Li, Qing
Transformer models have achieved remarkable success in sequential recommender systems (SRSs). However, computing the attention matrix in traditional dot-product attention mechanisms results in a quadratic complexity with sequence lengths, leading to
Externí odkaz:
http://arxiv.org/abs/2411.01537
Autor:
Wang, Maolin, Pan, Yu, Xu, Zenglin, Guo, Ruocheng, Zhao, Xiangyu, Wang, Wanyu, Wang, Yiqi, Liu, Zitao, Liu, Langming
Temporal Point Processes (TPPs) hold a pivotal role in modeling event sequences across diverse domains, including social networking and e-commerce, and have significantly contributed to the advancement of recommendation systems and information retrie
Externí odkaz:
http://arxiv.org/abs/2402.00388
Akademický článek
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