Search Results for author: Shuyuan Xu

Found 30 papers, 20 papers with code

AutoFlow: Automated Workflow Generation for Large Language Model Agents

1 code implementation1 Jul 2024 Zelong Li, Shuyuan Xu, Kai Mei, Wenyue Hua, Balaji Rama, Om Raheja, Hao Wang, He Zhu, Yongfeng Zhang

We believe that the automatic generation and interpretation of workflows in natural language represent a promising paradigm for solving complex tasks, particularly with the rapid development of LLMs.

AI Agent Language Modelling

Survey for Landing Generative AI in Social and E-commerce Recsys -- the Industry Perspectives

no code implementations10 Jun 2024 Da Xu, Danqing Zhang, Guangyu Yang, Bo Yang, Shuyuan Xu, Lingling Zheng, Cindy Liang

Recently, generative AI (GAI), with their emerging capabilities, have presented unique opportunities for augmenting and revolutionizing industrial recommender systems (Recsys).

Recommendation Systems

AIOS Compiler: LLM as Interpreter for Natural Language Programming and Flow Programming of AI Agents

3 code implementations11 May 2024 Shuyuan Xu, Zelong Li, Kai Mei, Yongfeng Zhang

However, the inherent vagueness, ambiguity, and verbosity of natural language pose significant challenges in developing an interpreter that can accurately understand the programming logic and execute instructions written in natural language.

IDGenRec: LLM-RecSys Alignment with Textual ID Learning

1 code implementation27 Mar 2024 Juntao Tan, Shuyuan Xu, Wenyue Hua, Yingqiang Ge, Zelong Li, Yongfeng Zhang

The results show that the zero-shot performance of the pre-trained foundation model is comparable to or even better than some traditional recommendation models based on supervised training, showing the potential of the IDGen paradigm serving as the foundation model for generative recommendation.

Sequential Recommendation Text Generation

AIOS: LLM Agent Operating System

1 code implementation25 Mar 2024 Kai Mei, Xi Zhu, Wujiang Xu, Wenyue Hua, Mingyu Jin, Zelong Li, Shuyuan Xu, Ruosong Ye, Yingqiang Ge, Yongfeng Zhang

This AIOS kernel provides fundamental services (e. g., scheduling, context management, memory management, storage management, access control) and efficient management of resources (e. g., LLM and external tools) for runtime agents.

AI Agent Language Modelling +2

LLM as OS, Agents as Apps: Envisioning AIOS, Agents and the AIOS-Agent Ecosystem

1 code implementation6 Dec 2023 Yingqiang Ge, Yujie Ren, Wenyue Hua, Shuyuan Xu, Juntao Tan, Yongfeng Zhang

We envision that LLM's impact will not be limited to the AI application level, instead, it will in turn revolutionize the design and implementation of computer system, architecture, software, and programming language, featured by several main concepts: LLM as OS (system-level), Agents as Applications (application-level), Natural Language as Programming Interface (user-level), and Tools as Devices/Libraries (hardware/middleware-level).

AI Agent Language Modelling

Language is All a Graph Needs

1 code implementation14 Aug 2023 Ruosong Ye, Caiqi Zhang, Runhui Wang, Shuyuan Xu, Yongfeng Zhang

The emergence of large-scale pre-trained language models has revolutionized various AI research domains.

Graph Learning Language Modelling

GenRec: Large Language Model for Generative Recommendation

1 code implementation2 Jul 2023 Jianchao Ji, Zelong Li, Shuyuan Xu, Wenyue Hua, Yingqiang Ge, Juntao Tan, Yongfeng Zhang

Subsequently, we use these prompts to fine-tune the LLaMA backbone LLM on a dataset of user-item interactions, represented by textual data, to capture user preferences and item characteristics.

Language Modelling Large Language Model +1

Counterfactual Collaborative Reasoning

no code implementations30 Jun 2023 Jianchao Ji, Zelong Li, Shuyuan Xu, Max Xiong, Juntao Tan, Yingqiang Ge, Hao Wang, Yongfeng Zhang

In this paper, we explore how the two reasoning abilities can be jointly modeled to enhance both accuracy and explainability of machine learning models.

counterfactual Counterfactual Reasoning +3

OpenP5: An Open-Source Platform for Developing, Training, and Evaluating LLM-based Recommender Systems

4 code implementations19 Jun 2023 Shuyuan Xu, Wenyue Hua, Yongfeng Zhang

In recent years, the integration of Large Language Models (LLMs) into recommender systems has garnered interest among both practitioners and researchers.

Benchmarking Decoder +2

UP5: Unbiased Foundation Model for Fairness-aware Recommendation

1 code implementation20 May 2023 Wenyue Hua, Yingqiang Ge, Shuyuan Xu, Jianchao Ji, Yongfeng Zhang

In this paper, we dive into the extent of unfairness exhibited by LLM-based recommender models based on both T5 and LLaMA backbones, and discuss appropriate methods for promoting equitable treatment of users in LLM-based recommendation models.

Decision Making Fairness +1

How to Index Item IDs for Recommendation Foundation Models

4 code implementations11 May 2023 Wenyue Hua, Shuyuan Xu, Yingqiang Ge, Yongfeng Zhang

To emphasize the importance of item indexing, we first discuss the issues of several trivial item indexing methods, such as random indexing, title indexing, and independent indexing.

Language Modelling

OpenAGI: When LLM Meets Domain Experts

2 code implementations NeurIPS 2023 Yingqiang Ge, Wenyue Hua, Kai Mei, Jianchao Ji, Juntao Tan, Shuyuan Xu, Zelong Li, Yongfeng Zhang

This capability is vital for Artificial Intelligence (AI) and should be embedded in comprehensive AI Agents, enabling them to harness expert models for complex task-solving towards Artificial General Intelligence (AGI).

Benchmarking Natural Language Queries

Causal Inference for Recommendation: Foundations, Methods and Applications

no code implementations8 Jan 2023 Shuyuan Xu, Jianchao Ji, Yunqi Li, Yingqiang Ge, Juntao Tan, Yongfeng Zhang

We discuss the fundamental concepts of both recommender systems and causal inference as well as their relationship, and review the existing work on causal methods for different problems in recommender systems.

Causal Inference Fairness +1

Causal Structure Learning with Recommendation System

no code implementations19 Oct 2022 Shuyuan Xu, Da Xu, Evren Korpeoglu, Sushant Kumar, Stephen Guo, Kannan Achan, Yongfeng Zhang

Discovering the causal mechanism from RS feedback data is both novel and challenging, since RS itself is a source of intervention that can influence both the users' exposure and their willingness to interact.

Decision Making Recommendation Systems

Dynamic Causal Collaborative Filtering

1 code implementation23 Aug 2022 Shuyuan Xu, Juntao Tan, Zuohui Fu, Jianchao Ji, Shelby Heinecke, Yongfeng Zhang

As a result, it is important to incorporate loops into the causal graphs to accurately model the dynamic and iterative data generation process for recommender systems.

Collaborative Filtering counterfactual +2

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 +3

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 +2

Learning and Evaluating Graph Neural Network Explanations based on Counterfactual and Factual Reasoning

1 code implementation17 Feb 2022 Juntao Tan, Shijie Geng, Zuohui Fu, Yingqiang Ge, Shuyuan Xu, Yunqi Li, Yongfeng Zhang

For quantitatively evaluating the generated explanations without the requirement of ground-truth, we design metrics based on Counterfactual and Factual reasoning to evaluate the necessity and sufficiency of the explanations.

Causal Inference counterfactual +1

Deconfounded Causal Collaborative Filtering

1 code implementation14 Oct 2021 Shuyuan Xu, Juntao Tan, Shelby Heinecke, Jia Li, Yongfeng Zhang

Experiments on real-world datasets show that our method is able to deconfound unobserved confounders to achieve better recommendation performance.

Collaborative Filtering Recommendation Systems

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

Counterfactual Explainable Recommendation

2 code implementations24 Aug 2021 Juntao Tan, Shuyuan Xu, Yingqiang Ge, Yunqi Li, Xu Chen, Yongfeng Zhang

Technically, for each item recommended to each user, CountER formulates a joint optimization problem to generate minimal changes on the item aspects so as to create a counterfactual item, such that the recommendation decision on the counterfactual item is reversed.

Causal Inference counterfactual +5

Personalized Counterfactual Fairness in Recommendation

1 code implementation20 May 2021 Yunqi Li, Hanxiong Chen, Shuyuan Xu, Yingqiang Ge, Yongfeng Zhang

Therefore, it is important to provide personalized fair recommendations for users to satisfy their personalized fairness demands.

counterfactual Decision Making +2

Efficient Non-Sampling Knowledge Graph Embedding

1 code implementation21 Apr 2021 Zelong Li, Jianchao Ji, Zuohui Fu, Yingqiang Ge, Shuyuan Xu, Chong Chen, Yongfeng Zhang

Experiments on benchmark datasets show that our NS-KGE framework can achieve a better performance on efficiency and accuracy over traditional negative sampling based models, and that the framework is applicable to a large class of knowledge graph embedding models.

Knowledge Graph Embedding

Causal Collaborative Filtering

1 code implementation3 Feb 2021 Shuyuan Xu, Yingqiang Ge, Yunqi Li, Zuohui Fu, Xu Chen, Yongfeng Zhang

However, pure correlative learning may lead to Simpson's paradox in predictions, and thus results in sacrificed recommendation performance.

Collaborative Filtering counterfactual +1

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

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

HID: Hierarchical Multiscale Representation Learning for Information Diffusion

2 code implementations19 Apr 2020 Honglu Zhou, Shuyuan Xu, Zuohui Fu, Gerard de Melo, Yongfeng Zhang, Mubbasir Kapadia

In this paper, we present a Hierarchical Information Diffusion (HID) framework by integrating user representation learning and multiscale modeling.

Representation Learning

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