Search Results for author: Jiakai Tang

Found 5 papers, 3 papers with code

Robustness-enhanced Uplift Modeling with Adversarial Feature Desensitization

no code implementations7 Oct 2023 Zexu Sun, Bowei He, Ming Ma, Jiakai Tang, Yuchen Wang, Chen Ma, Dugang Liu

Specifically, our RUAD can more effectively alleviate the feature sensitivity of the uplift model through two customized modules, including a feature selection module with joint multi-label modeling to identify a key subset from the input features and an adversarial feature desensitization module using adversarial training and soft interpolation operations to enhance the robustness of the model against this selected subset of features.

feature selection Marketing

A Survey on Large Language Model based Autonomous Agents

2 code implementations22 Aug 2023 Lei Wang, Chen Ma, Xueyang Feng, Zeyu Zhang, Hao Yang, Jingsen Zhang, ZhiYuan Chen, Jiakai Tang, Xu Chen, Yankai Lin, Wayne Xin Zhao, Zhewei Wei, Ji-Rong Wen

In this paper, we present a comprehensive survey of these studies, delivering a systematic review of the field of LLM-based autonomous agents from a holistic perspective.

Language Modelling Large Language Model

User Behavior Simulation with Large Language Model based Agents

1 code implementation5 Jun 2023 Lei Wang, Jingsen Zhang, Hao Yang, ZhiYuan Chen, Jiakai Tang, Zeyu Zhang, Xu Chen, Yankai Lin, Ruihua Song, Wayne Xin Zhao, Jun Xu, Zhicheng Dou, Jun Wang, Ji-Rong Wen

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.

Language Modelling Large Language Model +2

Fairness-aware Cross-Domain Recommendation

no code implementations1 Feb 2023 Jiakai Tang, Xu Chen, Xueyang Feng

Cross-Domain Recommendation (CDR) is an effective way to alleviate the cold-start problem.

Fairness Recommendation Systems

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

2 code implementations15 Jun 2022 Wayne Xin Zhao, Yupeng Hou, Xingyu Pan, Chen Yang, Zeyu Zhang, Zihan Lin, Jingsen Zhang, Shuqing Bian, Jiakai Tang, Wenqi Sun, Yushuo Chen, Lanling Xu, Gaowei Zhang, Zhen Tian, Changxin Tian, Shanlei Mu, Xinyan Fan, Xu Chen, Ji-Rong Wen

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.

Benchmarking Data Augmentation +3

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