Search Results for author: Shuchang Liu

Found 20 papers, 8 papers with code

KuaiSim: A Comprehensive Simulator for Recommender Systems

1 code implementation NeurIPS 2023 Kesen Zhao, Shuchang Liu, Qingpeng Cai, Xiangyu Zhao, Ziru Liu, Dong Zheng, Peng Jiang, Kun Gai

For each task, KuaiSim also provides evaluation protocols and baseline recommendation algorithms that further serve as benchmarks for future research.

Reinforcement Learning (RL) Sequential Recommendation

A Large Language Model Enhanced Conversational Recommender System

no code implementations11 Aug 2023 Yue Feng, Shuchang Liu, Zhenghai Xue, Qingpeng Cai, Lantao Hu, Peng Jiang, Kun Gai, Fei Sun

For response generation, we utilize the generation ability of LLM as a language interface to better interact with users.

Language Modelling Large Language Model +2

Generative Flow Network for Listwise Recommendation

1 code implementation4 Jun 2023 Shuchang Liu, Qingpeng Cai, Zhankui He, Bowen Sun, Julian McAuley, Dong Zheng, Peng Jiang, Kun Gai

In this work, we aim to learn a policy that can generate sufficiently diverse item lists for users while maintaining high recommendation quality.

Recommendation Systems

VIP5: Towards Multimodal Foundation Models for Recommendation

1 code implementation23 May 2023 Shijie Geng, Juntao Tan, Shuchang Liu, Zuohui Fu, Yongfeng Zhang

In light of this, we propose the development of a multimodal foundation model (MFM) considering visual, textual, and personalization modalities under the P5 recommendation paradigm, thus named VIP5 (Visual P5), to unify various modalities and recommendation tasks.

Recommendation Systems

Multi-Task Recommendations with Reinforcement Learning

1 code implementation7 Feb 2023 Ziru Liu, Jiejie Tian, Qingpeng Cai, Xiangyu Zhao, Jingtong Gao, Shuchang Liu, Dayou Chen, Tonghao He, Dong Zheng, Peng Jiang, Kun Gai

To be specific, the RMTL structure can address the two aforementioned issues by (i) constructing an MTL environment from session-wise interactions and (ii) training multi-task actor-critic network structure, which is compatible with most existing MTL-based recommendation models, and (iii) optimizing and fine-tuning the MTL loss function using the weights generated by critic networks.

Multi-Task Learning Recommendation Systems +2

Exploration and Regularization of the Latent Action Space in Recommendation

1 code implementation7 Feb 2023 Shuchang Liu, Qingpeng Cai, Bowen Sun, Yuhao Wang, Ji Jiang, Dong Zheng, Kun Gai, Peng Jiang, Xiangyu Zhao, Yongfeng Zhang

To overcome this challenge, we propose a hyper-actor and critic learning framework where the policy decomposes the item list generation process into a hyper-action inference step and an effect-action selection step.

Recommendation Systems

Reinforcing User Retention in a Billion Scale Short Video Recommender System

no code implementations3 Feb 2023 Qingpeng Cai, Shuchang Liu, Xueliang Wang, Tianyou Zuo, Wentao Xie, Bin Yang, Dong Zheng, Peng Jiang, Kun Gai

In this paper, we choose reinforcement learning methods to optimize the retention as they are designed to maximize the long-term performance.

Recommendation Systems reinforcement-learning +1

Two-Stage Constrained Actor-Critic for Short Video Recommendation

no code implementations3 Feb 2023 Qingpeng Cai, Zhenghai Xue, Chi Zhang, Wanqi Xue, Shuchang Liu, Ruohan Zhan, Xueliang Wang, Tianyou Zuo, Wentao Xie, Dong Zheng, Peng Jiang, Kun Gai

One the one hand, the platforms aims at optimizing the users' cumulative watch time (main goal) in long term, which can be effectively optimized by Reinforcement Learning.

Recommendation Systems reinforcement-learning +2

PrefRec: Recommender Systems with Human Preferences for Reinforcing Long-term User Engagement

no code implementations6 Dec 2022 Wanqi Xue, Qingpeng Cai, Zhenghai Xue, Shuo Sun, Shuchang Liu, Dong Zheng, Peng Jiang, Kun Gai, Bo An

Though promising, the application of RL heavily relies on well-designed rewards, but designing rewards related to long-term user engagement is quite difficult.

Recommendation Systems Reinforcement Learning (RL)

A Survey on Trustworthy Recommender Systems

no code implementations25 Jul 2022 Yingqiang Ge, Shuchang Liu, Zuohui Fu, Juntao Tan, Zelong Li, Shuyuan Xu, Yunqi Li, Yikun Xian, Yongfeng Zhang

Recommender systems (RS), serving at the forefront of Human-centered AI, are widely deployed in almost every corner of the web and facilitate the human decision-making process.

Decision Making Explainable Recommendation +2

Fairness in Recommendation: Foundations, Methods and Applications

no code implementations26 May 2022 Yunqi Li, Hanxiong Chen, Shuyuan Xu, Yingqiang Ge, Juntao Tan, Shuchang Liu, Yongfeng Zhang

It first presents a brief introduction about fairness in basic machine learning tasks such as classification and ranking in order to provide a general overview of fairness research, as well as introduce the more complex situations and challenges that need to be considered when studying fairness in recommender systems.

Decision Making Fairness +1

Explainable Fairness in Recommendation

no code implementations24 Apr 2022 Yingqiang Ge, Juntao Tan, Yan Zhu, Yinglong Xia, Jiebo Luo, Shuchang Liu, Zuohui Fu, Shijie Geng, Zelong Li, Yongfeng Zhang

In this paper, we study the problem of explainable fairness, which helps to gain insights about why a system is fair or unfair, and guides the design of fair recommender systems with a more informed and unified methodology.

counterfactual Fairness +1

Graph Collaborative Reasoning

no code implementations27 Dec 2021 Hanxiong Chen, Yunqi Li, Shaoyun Shi, Shuchang Liu, He Zhu, Yongfeng Zhang

Graphs can represent relational information among entities and graph structures are widely used in many intelligent tasks such as search, recommendation, and question answering.

Link Prediction Logical Reasoning +2

Counterfactual Evaluation for Explainable AI

no code implementations5 Sep 2021 Yingqiang Ge, Shuchang Liu, Zelong Li, Shuyuan Xu, Shijie Geng, Yunqi Li, Juntao Tan, Fei Sun, Yongfeng Zhang

While recent years have witnessed the emergence of various explainable methods in machine learning, to what degree the explanations really represent the reasoning process behind the model prediction -- namely, the faithfulness of explanation -- is still an open problem.

counterfactual Counterfactual Reasoning

Variation Control and Evaluation for Generative SlateRecommendations

no code implementations26 Feb 2021 Shuchang Liu, Fei Sun, Yingqiang Ge, Changhua Pei, Yongfeng Zhang

Slate recommendation generates a list of items as a whole instead of ranking each item individually, so as to better model the intra-list positional biases and item relations.

Recommendation Systems

Discrete Knowledge Graph Embedding based on Discrete Optimization

no code implementations13 Jan 2021 Yunqi Li, Shuyuan Xu, Bo Liu, Zuohui Fu, Shuchang Liu, Xu Chen, Yongfeng Zhang

This paper proposes a discrete knowledge graph (KG) embedding (DKGE) method, which projects KG entities and relations into the Hamming space based on a computationally tractable discrete optimization algorithm, to solve the formidable storage and computation cost challenges in traditional continuous graph embedding methods.

Knowledge Graph Embedding

Towards Long-term Fairness in Recommendation

1 code implementation10 Jan 2021 Yingqiang Ge, Shuchang Liu, Ruoyuan Gao, Yikun Xian, Yunqi Li, Xiangyu Zhao, Changhua Pei, Fei Sun, Junfeng Ge, Wenwu Ou, Yongfeng Zhang

We focus on the fairness of exposure of items in different groups, while the division of the groups is based on item popularity, which dynamically changes over time in the recommendation process.

Fairness Recommendation Systems

Learning Personalized Risk Preferences for Recommendation

1 code implementation6 Jul 2020 Yingqiang Ge, Shuyuan Xu, Shuchang Liu, Zuohui Fu, Fei Sun, Yongfeng Zhang

Before making purchases on e-commerce websites, most consumers tend to rely on rating scores and review information to make purchase decisions.

Recommendation Systems

Learning Post-Hoc Causal Explanations for Recommendation

no code implementations30 Jun 2020 Shuyuan Xu, Yunqi Li, Shuchang Liu, Zuohui Fu, Xu Chen, Yongfeng Zhang

State-of-the-art recommender systems have the ability to generate high-quality recommendations, but usually cannot provide intuitive explanations to humans due to the usage of black-box prediction models.

counterfactual Sequential Recommendation

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