Výsledky vyhledávání - "Tang, Jiakai"

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GenSim: A General Social Simulation Platform with Large Language Model based Agents

Autor: Tang, Jiakai, Gao, Heyang, Pan, Xuchen, Wang, Lei, Tan, Haoran, Gao, Dawei, Chen, Yushuo, Chen, Xu, Lin, Yankai, Li, Yaliang, Ding, Bolin, Zhou, Jingren, Wang, Jun, Wen, Ji-Rong

With the rapid advancement of large language models (LLMs), recent years have witnessed many promising studies on leveraging LLM-based agents to simulate human social behavior. While prior work has demonstrated significant potential across various do

Externí odkaz: http://arxiv.org/abs/2410.04360

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Towards Robust Recommendation via Decision Boundary-aware Graph Contrastive Learning

Autor: Tang, Jiakai, Dai, Sunhao, Sun, Zexu, Chen, Xu, Xu, Jun, Yu, Wenhui, Hu, Lantao, Jiang, Peng, Li, Han

In recent years, graph contrastive learning (GCL) has received increasing attention in recommender systems due to its effectiveness in reducing bias caused by data sparsity. However, most existing GCL models rely on heuristic approaches and usually a

Externí odkaz: http://arxiv.org/abs/2407.10184

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Robustness-enhanced Uplift Modeling with Adversarial Feature Desensitization

Autor: Sun, Zexu, He, Bowei, Ma, Ming, Tang, Jiakai, Wang, Yuchen, Ma, Chen, Liu, Dugang

Uplift modeling has shown very promising results in online marketing. However, most existing works are prone to the robustness challenge in some practical applications. In this paper, we first present a possible explanation for the above phenomenon.

Externí odkaz: http://arxiv.org/abs/2310.04693

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A Survey on Large Language Model based Autonomous Agents

Autor: Wang, Lei, Ma, Chen, Feng, Xueyang, Zhang, Zeyu, Yang, Hao, Zhang, Jingsen, Chen, Zhiyuan, Tang, Jiakai, Chen, Xu, Lin, Yankai, Zhao, Wayne Xin, Wei, Zhewei, Wen, Ji-Rong

Autonomous agents have long been a prominent research focus in both academic and industry communities. Previous research in this field often focuses on training agents with limited knowledge within isolated environments, which diverges significantly

Externí odkaz: http://arxiv.org/abs/2308.11432

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User Behavior Simulation with Large Language Model based Agents

Autor: Wang, Lei, Zhang, Jingsen, Yang, Hao, Chen, Zhiyuan, Tang, Jiakai, Zhang, Zeyu, Chen, Xu, Lin, Yankai, Song, Ruihua, Zhao, Wayne Xin, Xu, Jun, Dou, Zhicheng, Wang, Jun, Wen, Ji-Rong

Simulating high quality user behavior data has always been a fundamental problem in human-centered applications, where the major difficulty originates from the intricate mechanism of human decision process. Recently, substantial evidences have sugges

Externí odkaz: http://arxiv.org/abs/2306.02552

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Fairness-aware Cross-Domain Recommendation

Autor: Tang, Jiakai, Chen, Xu, Feng, Xueyang

Cross-Domain Recommendation (CDR) is an effective way to alleviate the cold-start problem. However, previous work severely ignores fairness and bias when learning the mapping function, which is used to obtain the representations for fresh users in th

Externí odkaz: http://arxiv.org/abs/2302.00158

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RecBole 2.0: Towards a More Up-to-Date Recommendation Library

Autor: Zhao, Wayne Xin, Hou, Yupeng, Pan, Xingyu, Yang, Chen, Zhang, Zeyu, Lin, Zihan, Zhang, Jingsen, Bian, Shuqing, Tang, Jiakai, Sun, Wenqi, Chen, Yushuo, Xu, Lanling, Zhang, Gaowei, Tian, Zhen, Tian, Changxin, Mu, Shanlei, Fan, Xinyan, Chen, Xu, Wen, Ji-Rong

In order to support the study of recent advances in recommender systems, this paper presents an extended recommendation library consisting of eight packages for up-to-date topics and architectures. First of all, from a data perspective, we consider t

Externí odkaz: http://arxiv.org/abs/2206.07351

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