Zobrazeno 1 - 10
of 189
pro vyhledávání: '"Zhu, Chenxu"'
Autor:
Liu, Qijiong, Dong, Xiaoyu, Xiao, Jiaren, Chen, Nuo, Hu, Hengchang, Zhu, Jieming, Zhu, Chenxu, Sakai, Tetsuya, Wu, Xiao-Ming
Vector quantization, renowned for its unparalleled feature compression capabilities, has been a prominent topic in signal processing and machine learning research for several decades and remains widely utilized today. With the emergence of large mode
Externí odkaz:
http://arxiv.org/abs/2405.03110
Autor:
Wang, Hangyu, Lin, Jianghao, Li, Xiangyang, Chen, Bo, Zhu, Chenxu, Tang, Ruiming, Zhang, Weinan, Yu, Yong
Click-through rate (CTR) prediction plays as a core function module in various personalized online services. The traditional ID-based models for CTR prediction take as inputs the one-hot encoded ID features of tabular modality, which capture the coll
Externí odkaz:
http://arxiv.org/abs/2310.19453
Autor:
Lin, Jianghao, Shan, Rong, Zhu, Chenxu, Du, Kounianhua, Chen, Bo, Quan, Shigang, Tang, Ruiming, Yu, Yong, Zhang, Weinan
With large language models (LLMs) achieving remarkable breakthroughs in natural language processing (NLP) domains, LLM-enhanced recommender systems have received much attention and have been actively explored currently. In this paper, we focus on ada
Externí odkaz:
http://arxiv.org/abs/2308.11131
Autor:
Lin, Jianghao, Dai, Xinyi, Xi, Yunjia, Liu, Weiwen, Chen, Bo, Zhang, Hao, Liu, Yong, Wu, Chuhan, Li, Xiangyang, Zhu, Chenxu, Guo, Huifeng, Yu, Yong, Tang, Ruiming, Zhang, Weinan
With the rapid development of online services, recommender systems (RS) have become increasingly indispensable for mitigating information overload. Despite remarkable progress, conventional recommendation models (CRM) still have some limitations, e.g
Externí odkaz:
http://arxiv.org/abs/2306.05817
Autor:
Li, Xiangyang, Chen, Bo, Guo, HuiFeng, Li, Jingjie, Zhu, Chenxu, Long, Xiang, Li, Sujian, Wang, Yichao, Guo, Wei, Mao, Longxia, Liu, Jinxing, Dong, Zhenhua, Tang, Ruiming
Scoring a large number of candidates precisely in several milliseconds is vital for industrial pre-ranking systems. Existing pre-ranking systems primarily adopt the \textbf{two-tower} model since the ``user-item decoupling architecture'' paradigm is
Externí odkaz:
http://arxiv.org/abs/2210.09890
Autor:
Ma, Handong, Hou, Jiawei, Zhu, Chenxu, Zhang, Weinan, Tang, Ruiming, Lai, Jincai, Zhu, Jieming, He, Xiuqiang, Yu, Yong
Pseudo relevance feedback (PRF) automatically performs query expansion based on top-retrieved documents to better represent the user's information need so as to improve the search results. Previous PRF methods mainly select expansion terms with high
Externí odkaz:
http://arxiv.org/abs/2111.08229
Autor:
Zhu, Chenxu, Chen, Bo, Zhang, Weinan, Lai, Jincai, Tang, Ruiming, He, Xiuqiang, Li, Zhenguo, Yu, Yong
Feature embedding learning and feature interaction modeling are two crucial components of deep models for Click-Through Rate (CTR) prediction. Most existing deep CTR models suffer from the following three problems. First, feature interactions are eit
Externí odkaz:
http://arxiv.org/abs/2111.03318
Publikováno v:
In Micro and Nano Engineering March 2024 22
Autor:
Boldrini, Maura, Xiao, Yang, Sing, Tarjinder, Zhu, Chenxu, Jabbi, Mbemba, Pantazopoulos, Harry, Gürsoy, Gamze, Martinowich, Keri, Punzi, Giovanna, Vallender, Eric J., Zody, Michael, Berretta, Sabina, Hyde, Thomas M., Kleinman, Joel E., Marenco, Stefano, Roussos, Panagiotis, Lewis, David A., Turecki, Gustavo, Lehner, Thomas, Mann, J. John
Publikováno v:
In Biological Psychiatry May 2024
Autor:
Lu, Mingxing, Zhu, Chenxu, Smetana, Sergiy, Zhao, Ming, Zhang, Haibo, Zhang, Fang, Du, Yuzhou
Publikováno v:
In Food Science and Human Wellness January 2024 13(1):65-74