Zobrazeno 1 - 10
of 74
pro vyhledávání: '"Zheng, Chunyuan"'
Ratings of a user to most items in recommender systems are usually missing not at random (MNAR), largely because users are free to choose which items to rate. To achieve unbiased learning of the prediction model under MNAR data, three typical solutio
Externí odkaz:
http://arxiv.org/abs/2406.17182
Selection bias in recommender system arises from the recommendation process of system filtering and the interactive process of user selection. Many previous studies have focused on addressing selection bias to achieve unbiased learning of the predict
Externí odkaz:
http://arxiv.org/abs/2404.19620
Debiased collaborative filtering aims to learn an unbiased prediction model by removing different biases in observational datasets. To solve this problem, one of the simple and effective methods is based on the propensity score, which adjusts the obs
Externí odkaz:
http://arxiv.org/abs/2404.19596
Multimedia recommendation involves personalized ranking tasks, where multimedia content is usually represented using a generic encoder. However, these generic representations introduce spurious correlations that fail to reveal users' true preferences
Externí odkaz:
http://arxiv.org/abs/2308.04706
Autor:
Li, Haoxuan, Hu, Taojun, Xiong, Zetong, Zheng, Chunyuan, Feng, Fuli, He, Xiangnan, Zhou, Xiao-Hua
Adverse drug reaction (ADR) prediction plays a crucial role in both health care and drug discovery for reducing patient mortality and enhancing drug safety. Recently, many studies have been devoted to effectively predict the drug-ADRs incidence rates
Externí odkaz:
http://arxiv.org/abs/2308.02571
Publikováno v:
Proceedings of the ACM Web Conference 2023 (WWW '23), April 30-May 4, 2023, Austin, TX, USA
Recommender systems are seen as an effective tool to address information overload, but it is widely known that the presence of various biases makes direct training on large-scale observational data result in sub-optimal prediction performance. In con
Externí odkaz:
http://arxiv.org/abs/2304.09085
Autor:
Shen, Xuejian, Borrow, Josh, Vogelsberger, Mark, Garaldi, Enrico, Smith, Aaron, Kannan, Rahul, Tacchella, Sandro, Zavala, Jesús, Hernquist, Lars, Yeh, Jessica Y. -C., Zheng, Chunyuan
Using high-resolution cosmological radiation-hydrodynamic (RHD) simulations (THESAN-HR), we explore the impact of alternative dark matter (altDM) models on galaxies during the Epoch of Reionization. The simulations adopt the IllustrisTNG galaxy forma
Externí odkaz:
http://arxiv.org/abs/2304.06742
In recommender systems, users always choose the favorite items to rate, which leads to data missing not at random and poses a great challenge for unbiased evaluation and learning of prediction models. Currently, the doubly robust (DR) methods have be
Externí odkaz:
http://arxiv.org/abs/2205.04701
Bias is a common problem inherent in recommender systems, which is entangled with users' preferences and poses a great challenge to unbiased learning. For debiasing tasks, the doubly robust (DR) method and its variants show superior performance due t
Externí odkaz:
http://arxiv.org/abs/2203.10258
Autor:
Wei, Hanze, Zheng, Ziao, Xu, Xiaoling, Zheng, Chunyuan, Li, Bin, Zhao, Bingchen, Wei, Ziqing, Zhai, Xiaoqiang
Publikováno v:
In Applied Thermal Engineering 15 December 2024 257 Part C
