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References (18)

[1]
CIREC: Causal Intervention-Inspired Policy Learning to Mitigate Exposure Bias for Interactive Recommendation
2026Yongsen Zheng, Guohua Wang et al.
[2]
Maximum Entropy Policy for Long-Term Fairness in Interactive Recommender Systems
2024Xiaoyu Shi, Quanliang Liu et al.
[3]
KuaiSim: A Comprehensive Simulator for Recommender Systems
2023Kesen Zhao, Shuchang Liu et al.
[4]
Alleviating Matthew Effect of Offline Reinforcement Learning in Interactive Recommendation
2023Chongming Gao, Kexin Huang et al.
[5]
DiffuRec: A Diffusion Model for Sequential Recommendation
2023Zihao Li, Aixin Sun et al.
[6]
KuaiRand: An Unbiased Sequential Recommendation Dataset with Randomly Exposed Videos
2022Chongming Gao, Shijun Li et al.
[7]
A Survey on the Fairness of Recommender Systems
2022Yifan Wang, Weizhi Ma et al.
[8]
KuaiRec: A Fully-observed Dataset for Recommender Systems
2022Chongming Gao, Shijun Li et al.
[9]
Towards Long-term Fairness in Recommendation
2021Yingqiang Ge, Shuchang Liu et al.
[10]
Denoising Implicit Feedback for Recommendation
2020Wenjie Wang, Fuli Feng et al.
[11]
Degenerate Feedback Loops in Recommender Systems
2019Ray Jiang, S. Chiappa et al.
[12]
Off-Policy Deep Reinforcement Learning without Exploration
2018Scott Fujimoto, D. Meger et al.
[13]
Stabilizing Reinforcement Learning in Dynamic Environment with Application to Online Recommendation
2018Shi-Yong Chen, Yang Yu et al.
[14]
Learning to Reweight Examples for Robust Deep Learning
2018Mengye Ren, Wenyuan Zeng et al.
[15]
Addressing Function Approximation Error in Actor-Critic Methods
2018Scott Fujimoto, H. V. Hoof et al.
[16]
Deep Reinforcement Learning for List-wise Recommendations
2017Xiangyu Zhao, L. Zhang et al.
[17]
Collaborative Denoising Auto-Encoders for Top-N Recommender Systems
2016Yao Wu, Christopher DuBois et al.
[18]
Asynchronous Methods for Deep Reinforcement Learning
2016Volodymyr Mnih, Adrià Puigdomènech Badia et al.

Founder's Pitch

"Develop a fairness-oriented interactive recommendation system leveraging denoised latent preferences and hierarchical decision-making."

Recommender SystemsScore: 4View PDF ↗

Commercial Viability Breakdown

0-10 scale

High Potential

1/4 signals

2.5

Quick Build

2/4 signals

5

Series A Potential

1/4 signals

2.5

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