9 citations · 16 across the 7 of their papers we have counts for
8 papers
Tlow: Flow-based Item Tokenizer for Recommendation
Nian Li, Chonggang Song, Jingtao Ding +3
Item tokenizer encodes semantic embeddings into token IDs to replace the randomly assigned item IDs used in traditional recommendation models, fundamentally addressing the problems…
TetherCache: Stabilizing Autoregressive Long-Form Video Generation with Gated Recall and Trusted Alignment
Yu Meng, Xiangyang Luo, Letian Li +5
Autoregressive video diffusion models provide a natural formulation for streaming and variable-length video generation by conditioning newly generated frames on previously generate…
An Evaluation of Cultural Value Alignment in LLM
Nicholas Sukiennik, Chen Gao, Fengli Xu +1
LLMs as intelligent agents are being increasingly applied in scenarios where human interactions are involved, leading to a critical concern about whether LLMs are faithful to the v…
Simulating Filter Bubble on Short-video Recommender System with Large Language Model Agents
Nicholas Sukiennik, Haoyu Wang, Zailin Zeng +2
An increasing reliance on recommender systems has led to concerns about the creation of filter bubbles on social media, especially on short video platforms like TikTok. However, th…
A Large-scale Dataset with Behavior, Attributes, and Content of Mobile Short-video Platform
Yu Shang, Chen Gao, Nian Li +1
Short-video platforms show an increasing impact on people's daily lives nowadays, with billions of active users spending plenty of time each day. The interactions between users and…
AgentSociety: Large-Scale Simulation of LLM-Driven Generative Agents Advances Understanding of Human Behaviors and Society
Jinghua Piao, Yuwei Yan, Jun Zhang +13
Understanding human behavior and society is a central focus in social sciences, with the rise of generative social science marking a significant paradigmatic shift. By leveraging b…
Modeling User Fatigue for Sequential Recommendation
Nian Li, Xin Ban, Cheng Ling +6
Recommender systems filter out information that meets user interests. However, users may be tired of the recommendations that are too similar to the content they have been exposed…
EconAgent: Large Language Model-Empowered Agents for Simulating Macroeconomic Activities
Nian Li, Chen Gao, Mingyu Li +2
The advent of artificial intelligence has led to a growing emphasis on data-driven modeling in macroeconomics, with agent-based modeling (ABM) emerging as a prominent bottom-up sim…