Zobrazeno 1 - 7
of 7
pro vyhledávání: '"Dong, Daxiang"'
In recommendation systems, new items are continuously introduced, initially lacking interaction records but gradually accumulating them over time. Accurately predicting the click-through rate (CTR) for these items is crucial for enhancing both revenu
Externí odkaz:
http://arxiv.org/abs/2407.10112
Making personalized recommendation for cold-start users, who only have a few interaction histories, is a challenging problem in recommendation systems. Recent works leverage hypernetworks to directly map user interaction histories to user-specific pa
Externí odkaz:
http://arxiv.org/abs/2306.03387
Autor:
Liu, Ji, Dong, Daxiang, Wang, Xi, Qin, An, Li, Xingjian, Valduriez, Patrick, Dou, Dejing, Yu, Dianhai
Although more layers and more parameters generally improve the accuracy of the models, such big models generally have high computational complexity and require big memory, which exceed the capacity of small devices for inference and incurs long train
Externí odkaz:
http://arxiv.org/abs/2207.06667
Autor:
Liu, Hao, Gao, Qian, Li, Jiang, Liao, Xiaochao, Xiong, Hao, Chen, Guangxing, Wang, Wenlin, Yang, Guobao, Zha, Zhiwei, Dong, Daxiang, Dou, Dejing, Xiong, Haoyi
In modern internet industries, deep learning based recommender systems have became an indispensable building block for a wide spectrum of applications, such as search engine, news feed, and short video clips. However, it remains challenging to carry
Externí odkaz:
http://arxiv.org/abs/2106.01674
Autor:
Qu, Yingqi, Ding, Yuchen, Liu, Jing, Liu, Kai, Ren, Ruiyang, Zhao, Wayne Xin, Dong, Daxiang, Wu, Hua, Wang, Haifeng
In open-domain question answering, dense passage retrieval has become a new paradigm to retrieve relevant passages for finding answers. Typically, the dual-encoder architecture is adopted to learn dense representations of questions and passages for s
Externí odkaz:
http://arxiv.org/abs/2010.08191
In this paper we propose to solve an important problem in recommendation -- user cold start, based on meta leaning method. Previous meta learning approaches finetune all parameters for each new user, which is both computing and storage expensive. In
Externí odkaz:
http://arxiv.org/abs/1912.04108
Autor:
Liu, Ji, Dong, Daxiang, Wang, Xi, Qin, An, Li, Xingjian, Valduriez, Patrick, Dou, Dejing, Yu, Dianhai
Publikováno v:
Concurrency & Computation: Practice & Experience; Nov2023, Vol. 35 Issue 26, p1-14, 14p
