Zobrazeno 1 - 10
of 856
pro vyhledávání: '"WANG Maolin"'
Autor:
Liu, Ziwei, Liu, Qidong, Wang, Yejing, Wang, Wanyu, Jia, Pengyue, Wang, Maolin, Liu, Zitao, Chang, Yi, Zhao, Xiangyu
In various domains, Sequential Recommender Systems (SRS) have become essential due to their superior capability to discern intricate user preferences. Typically, SRS utilize transformer-based architectures to forecast the subsequent item within a seq
Externí odkaz:
http://arxiv.org/abs/2408.11451
Autor:
Yao, Jiacheng, Wang, Maolin, Chen, Wanqi, Jin, Chengxiang, Zhou, Jiajun, Yu, Shanqing, Xuan, Qi
The wide application of Ethereum technology has brought technological innovation to traditional industries. As one of Ethereum's core applications, smart contracts utilize diverse contract codes to meet various functional needs and have gained widesp
Externí odkaz:
http://arxiv.org/abs/2407.00336
In the rapidly evolving field of artificial intelligence, transformer-based models have gained significant attention in the context of Sequential Recommender Systems (SRSs), demonstrating remarkable proficiency in capturing user-item interactions. Ho
Externí odkaz:
http://arxiv.org/abs/2406.10244
Autor:
Luo, Sichun, Shao, Wei, Yao, Yuxuan, Xu, Jian, Liu, Mingyang, Li, Qintong, He, Bowei, Wang, Maolin, Deng, Guanzhi, Hou, Hanxu, Zhang, Xinyi, Song, Linqi
Nowadays, large language models (LLMs) have been integrated with conventional recommendation models to improve recommendation performance. However, while most of the existing works have focused on improving the model performance, the privacy issue ha
Externí odkaz:
http://arxiv.org/abs/2406.01363
Autor:
Zhang, Sheng, Wang, Maolin, Zhao, Yao, Zhuang, Chenyi, Gu, Jinjie, Guo, Ruocheng, Zhao, Xiangyu, Zhang, Zijian, Yin, Hongzhi
In this age where data is abundant, the ability to distill meaningful insights from the sea of information is essential. Our research addresses the computational and resource inefficiencies that current Sequential Recommender Systems (SRSs) suffer fr
Externí odkaz:
http://arxiv.org/abs/2402.00390
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
Autor:
Wang, Maolin, Zhao, Yao, Liu, Jiajia, Chen, Jingdong, Zhuang, Chenyi, Gu, Jinjie, Guo, Ruocheng, Zhao, Xiangyu
The deployment of Large Multimodal Models (LMMs) within AntGroup has significantly advanced multimodal tasks in payment, security, and advertising, notably enhancing advertisement audition tasks in Alipay. However, the deployment of such sizable mode
Externí odkaz:
http://arxiv.org/abs/2312.05795
Knowledge graphs (KGs), which consist of triples, are inherently incomplete and always require completion procedure to predict missing triples. In real-world scenarios, KGs are distributed across clients, complicating completion tasks due to privacy
Externí odkaz:
http://arxiv.org/abs/2311.10341
Autor:
Zhou, You, Lin, Xiujing, Zhang, Xiang, Wang, Maolin, Jiang, Gangwei, Lu, Huakang, Wu, Yupeng, Zhang, Kai, Yang, Zhe, Wang, Kehang, Sui, Yongduo, Jia, Fengwei, Tang, Zuoli, Zhao, Yao, Zhang, Hongxuan, Yang, Tiannuo, Chen, Weibo, Mao, Yunong, Li, Yi, Bao, De, Li, Yu, Liao, Hongrui, Liu, Ting, Liu, Jingwen, Guo, Jinchi, Zhao, Xiangyu, WEI, Ying, Qian, Hong, Liu, Qi, Wang, Xiang, Kin, Wai, Chan, Li, Chenliang, Li, Yusen, Yang, Shiyu, Yan, Jining, Mou, Chao, Han, Shuai, Jin, Wuxia, Zhang, Guannan, Zeng, Xiaodong
Artificial Intelligence (AI) has achieved significant advancements in technology and research with the development over several decades, and is widely used in many areas including computing vision, natural language processing, time-series analysis, s
Externí odkaz:
http://arxiv.org/abs/2311.00447
Autor:
Zhao, Xiangyu, Wang, Maolin, Zhao, Xinjian, Li, Jiansheng, Zhou, Shucheng, Yin, Dawei, Li, Qing, Tang, Jiliang, Guo, Ruocheng
Recommender systems have become an essential component of many online platforms, providing personalized recommendations to users. A crucial aspect is embedding techniques that coverts the high-dimensional discrete features, such as user and item IDs,
Externí odkaz:
http://arxiv.org/abs/2310.18608