Zobrazeno 1 - 10
of 870
pro vyhledávání: '"Wang, YiQi"'
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:
Han, Xiaotian, Jian, Yiren, Hu, Xuefeng, Liu, Haogeng, Wang, Yiqi, Fan, Qihang, Ai, Yuang, Huang, Huaibo, He, Ran, Yang, Zhenheng, You, Quanzeng
Pre-training on large-scale, high-quality datasets is crucial for enhancing the reasoning capabilities of Large Language Models (LLMs), especially in specialized domains such as mathematics. Despite the recognized importance, the Multimodal LLMs (MLL
Externí odkaz:
http://arxiv.org/abs/2409.12568
Top-k Nearest Neighbors (kNN) problem on road network has numerous applications on location-based services. As direct search using the Dijkstra's algorithm results in a large search space, a plethora of complex-index-based approaches have been propos
Externí odkaz:
http://arxiv.org/abs/2408.05432
Autor:
Yang, Xihong, Wang, Yiqi, Chen, Jin, Fan, Wenqi, Zhao, Xiangyu, Zhu, En, Liu, Xinwang, Lian, Defu
Deep learning has been widely applied in recommender systems, which has achieved revolutionary progress recently. However, most existing learning-based methods assume that the user and item distributions remain unchanged between the training phase an
Externí odkaz:
http://arxiv.org/abs/2407.15620
Knowledge of the domain of applicability of a machine learning model is essential to ensuring accurate and reliable model predictions. In this work, we develop a new approach of assessing model domain and demonstrate that our approach provides accura
Externí odkaz:
http://arxiv.org/abs/2406.05143
Autor:
Zhang, Jiaxin, Wang, Yiqi, Yang, Xihong, Wang, Siwei, Feng, Yu, Shi, Yu, Ren, Ruicaho, Zhu, En, Liu, Xinwang
Graph Neural Networks have demonstrated great success in various fields of multimedia. However, the distribution shift between the training and test data challenges the effectiveness of GNNs. To mitigate this challenge, Test-Time Training (TTT) has b
Externí odkaz:
http://arxiv.org/abs/2404.13571
Autor:
Liu, Haogeng, You, Quanzeng, Han, Xiaotian, Wang, Yiqi, Zhai, Bohan, Liu, Yongfei, Tao, Yunzhe, Huang, Huaibo, He, Ran, Yang, Hongxia
Multimodal Large Language Models (MLLMs) have experienced significant advancements recently. Nevertheless, challenges persist in the accurate recognition and comprehension of intricate details within high-resolution images. Despite being indispensabl
Externí odkaz:
http://arxiv.org/abs/2403.01487
Autor:
Hu, Jiaxi, Gao, Jingtong, Zhao, Xiangyu, Hu, Yuehong, Liang, Yuxuan, Wang, Yiqi, He, Ming, Liu, Zitao, Yin, Hongzhi
The integration of multimodal information into sequential recommender systems has attracted significant attention in recent research. In the initial stages of multimodal sequential recommendation models, the mainstream paradigm was ID-dominant recomm
Externí odkaz:
http://arxiv.org/abs/2402.17334
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