no code implementations • 7 Mar 2024 • Nicholas Sukiennik, Chen Gao, Nian Li
We formalize our definition of a "deep" filter bubble within this context, and then explore various correlations within the data: first understanding the evolution of the deep filter bubble over time, and later revealing some of the factors that give rise to this phenomenon, such as specific categories, user demographics, and feedback type.
no code implementations • 18 Feb 2024 • Lin Chen, Fengli Xu, Nian Li, Zhenyu Han, Meng Wang, Yong Li, Pan Hui
We propose a novel REasoning meta-STRUCTure search (ReStruct) framework that integrates LLM reasoning into the evolutionary procedure.
no code implementations • 19 Dec 2023 • Chen Gao, Xiaochong Lan, Nian Li, Yuan Yuan, Jingtao Ding, Zhilun Zhou, Fengli Xu, Yong Li
Finally, since this area is new and quickly evolving, we discuss the open problems and promising future directions.
no code implementations • 16 Oct 2023 • Nian Li, Chen Gao, Yong Li, Qingmin Liao
In this work, we take an early step in introducing a novel approach that leverages LLMs in macroeconomic simulation.
no code implementations • 28 Aug 2023 • Yuhan Quan, Jingtao Ding, Chen Gao, Nian Li, Lingling Yi, Depeng Jin, Yong Li
Micro-videos platforms such as TikTok are extremely popular nowadays.
no code implementations • 25 Aug 2023 • Yunzhu Pan, Nian Li, Chen Gao, Jianxin Chang, Yanan Niu, Yang song, Depeng Jin, Yong Li
Specifically, in short-video recommendation, the easiest-to-collect user feedback is the skipping behavior, which leads to two critical challenges for the recommendation model.
1 code implementation • 5 Nov 2021 • Zirui Zhu, Chen Gao, Xu Chen, Nian Li, Depeng Jin, Yong Li
With the hypergraph convolutional networks, the social relations can be modeled in a more fine-grained manner, which more accurately depicts real users' preferences, and benefits the recommendation performance.
no code implementations • submitted to TOIS 2021 • Chen Gao, Yu Zheng, Nian Li, Yinfeng Li, Yingrong Qin, Jinghua Piao, Yuhan Quan, Jianxin Chang, Depeng Jin, Xiangnan He, Yong Li
In this survey, we conduct a comprehensive review of the literature on graph neural network-based recommender systems.